From e3178720cd1a6969137456e83f0c5e2bbac77af4 Mon Sep 17 00:00:00 2001
From: Jae Yoo <jaeyoo@google.com>
Date: Wed, 15 Mar 2023 01:01:41 -0700
Subject: [PATCH 01/13] Upgrade bazel version to 5.1.0 for building with
 TFv2.11.0

---
 .bazelversion | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.bazelversion b/.bazelversion
index 0b2eb36f5..831446cbd 100644
--- a/.bazelversion
+++ b/.bazelversion
@@ -1 +1 @@
-3.7.2
+5.1.0

From 26fa2dfda35ab64585d03e20def1dfd42031b123 Mon Sep 17 00:00:00 2001
From: Jae Yoo <jaeyoo@google.com>
Date: Wed, 15 Mar 2023 01:05:11 -0700
Subject: [PATCH 02/13] Upgrade to TF v2.11

---
 WORKSPACE                                     |  10 +-
 benchmarks/README.md                          |   4 +-
 configure.sh                                  |   7 +-
 docs/install.md                               |  19 +--
 docs/tutorials/barren_plateaus.ipynb          |   2 +-
 docs/tutorials/gradients.ipynb                |   2 +-
 docs/tutorials/hello_many_worlds.ipynb        |  10 +-
 docs/tutorials/mnist.ipynb                    |   4 +-
 .../quantum_reinforcement_learning.ipynb      |   6 +-
 release/setup.py                              |   6 +-
 requirements.txt                              |   4 +-
 scripts/benchmark_all.sh                      |   4 +-
 scripts/build_pip_package_test.sh             |   2 +-
 scripts/msan_test.sh                          |   6 +-
 scripts/test_all.sh                           |   4 +-
 scripts/test_benchmarks.sh                    |   6 +-
 tensorflow_quantum/core/ops/BUILD             |  42 +++----
 tensorflow_quantum/core/ops/cirq_ops_test.py  |   2 +-
 tensorflow_quantum/core/ops/math_ops/BUILD    |   6 +-
 .../core/ops/math_ops/simulate_mps_test.py    |   5 +-
 .../core/ops/math_ops/tfq_inner_product.cc    |   4 +-
 .../ops/math_ops/tfq_inner_product_grad.cc    |   4 +-
 .../math_ops/tfq_simulate_1d_expectation.cc   |   6 +-
 .../tfq_simulate_1d_sampled_expectation.cc    |   6 +-
 .../ops/math_ops/tfq_simulate_1d_samples.cc   |   4 +-
 tensorflow_quantum/core/ops/noise/BUILD       |   6 +-
 .../core/ops/noise/tfq_noisy_expectation.cc   |   6 +-
 .../noise/tfq_noisy_sampled_expectation.cc    |   6 +-
 .../core/ops/noise/tfq_noisy_samples.cc       |   4 +-
 tensorflow_quantum/core/ops/parse_context.cc  |  71 ++++++-----
 .../core/ops/tfq_adj_grad_op.cc               |   4 +-
 .../core/ops/tfq_calculate_unitary_op.cc      |   4 +-
 .../core/ops/tfq_circuit_append_op.cc         |   2 +-
 .../core/ops/tfq_ps_decompose_op.cc           |   2 +-
 .../core/ops/tfq_ps_symbol_replace_op.cc      |   2 +-
 .../ops/tfq_ps_weights_from_symbols_op.cc     |   2 +-
 .../core/ops/tfq_resolve_parameters_op.cc     |   4 +-
 .../core/ops/tfq_simulate_expectation_op.cc   |   6 +-
 .../tfq_simulate_sampled_expectation_op.cc    |   6 +-
 .../core/ops/tfq_simulate_samples_op.cc       |   4 +-
 .../core/ops/tfq_simulate_state_op.cc         |   4 +-
 .../core/ops/tfq_utility_ops_test.py          |   2 +-
 .../core/serialize/op_serializer.py           |   2 +-
 .../core/serialize/op_serializer_test.py      |   2 +-
 tensorflow_quantum/core/src/BUILD             |   7 ++
 .../core/src/circuit_parser_qsim.cc           |  69 ++++++-----
 .../core/src/circuit_parser_qsim_test.cc      | 113 ++++++++++--------
 .../core/src/program_resolution.cc            |  52 ++++----
 .../core/src/program_resolution_test.cc       |  56 +++++----
 tensorflow_quantum/core/src/util_qsim.h       |  14 +--
 tensorflow_quantum/datasets/spin_system.py    |   4 +-
 .../datasets/spin_system_test.py              |   3 +-
 .../sampled_expectation_test.py               |   2 +-
 53 files changed, 352 insertions(+), 282 deletions(-)

diff --git a/WORKSPACE b/WORKSPACE
index b70cca65b..b47e25d2f 100644
--- a/WORKSPACE
+++ b/WORKSPACE
@@ -3,8 +3,8 @@
 
 load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")
 
-EIGEN_COMMIT = "12e8d57108c50d8a63605c6eb0144c838c128337"
-EIGEN_SHA256 = "f689246e342c3955af48d26ce74ac34d21b579a00675c341721a735937919b02"
+EIGEN_COMMIT = "3bb6a48d8c171cf20b5f8e48bfb4e424fbd4f79e"
+EIGEN_SHA256 = "eca9847b3fe6249e0234a342b78f73feec07d29f534e914ba5f920f3e09383a3"
 
 
 http_archive(
@@ -33,10 +33,10 @@ http_archive(
 
 http_archive(
     name = "org_tensorflow",
-    sha256 = "249b48ddee927801c7a4f8e5442cf1a3c860f6f46b85a2ff7a78b501507dd561",
-    strip_prefix = "tensorflow-2.7.0",
+    sha256 = "e52cda3bae45f0ae0fccd4055e9fa29892b414f70e2df94df9a3a10319c75fff",
+    strip_prefix = "tensorflow-2.11.0",
     urls = [
-        "https://github.com/tensorflow/tensorflow/archive/refs/tags/v2.7.0.zip",
+        "https://github.com/tensorflow/tensorflow/archive/refs/tags/v2.11.0.zip",
     ],
 )
 
diff --git a/benchmarks/README.md b/benchmarks/README.md
index b71bd1b04..4e71f8de2 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -24,7 +24,7 @@ Some notes on benchmark configuration:
 
 For example, to benchmark a dense depth-10 Clifford circuit over 5 qubits call:
 ```
-bazel run -c opt --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" --cxxopt="-msse2" \
+bazel run -c opt --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=1" --cxxopt="-msse2" \
   --cxxopt="-msse3" --cxxopt="-msse4" \
   benchmarks/scripts:benchmark_clifford_circuit -- \
   --n_moments 5 --n_qubits 4 \
@@ -39,7 +39,7 @@ benchmarks/scripts/reports/CliffordBenchmarks.benchmark_clifford_circuit_4_5_1
 To benchmark the parameter shift differentiation method on a random depth-10 4-qubit circuit with 10 parameters call, where the circuit will be differentiated
 over 50 trials, each time over a batch of 10 circuits.
 ```
-bazel run -c opt --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" --cxxopt="-msse2" \
+bazel run -c opt --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=1" --cxxopt="-msse2" \
   --cxxopt="-msse3" --cxxopt="-msse4" \
   benchmarks/scripts:benchmark_op_gradients -- \
   --n_moments 10 --n_qubits 4 --n_symbols 10 \
diff --git a/configure.sh b/configure.sh
index 6ba7a1bc4..5efd4212a 100755
--- a/configure.sh
+++ b/configure.sh
@@ -99,13 +99,12 @@ if [[ "$PIP_MANYLINUX2010" == "0" ]]; then
   write_to_bazelrc "build:cuda --crosstool_top=@local_config_cuda//crosstool:toolchain"
 fi
 
-
+write_to_bazelrc "build --experimental_repo_remote_exec"
 write_to_bazelrc "build --spawn_strategy=standalone"
 write_to_bazelrc "build --strategy=Genrule=standalone"
-write_to_bazelrc "build --experimental_repo_remote_exec"
 write_to_bazelrc "build -c opt"
-write_to_bazelrc "build --cxxopt=\"-D_GLIBCXX_USE_CXX11_ABI=0\""
-write_to_bazelrc "build --cxxopt=\"-std=c++14\""
+write_to_bazelrc "build --cxxopt=\"-D_GLIBCXX_USE_CXX11_ABI=1\""
+write_to_bazelrc "build --cxxopt=\"-std=c++17\""
 
 
 if is_windows; then
diff --git a/docs/install.md b/docs/install.md
index 8c19034c1..6d6efdda0 100644
--- a/docs/install.md
+++ b/docs/install.md
@@ -84,7 +84,7 @@ As noted in the TensorFlow
 guide, the <a href="https://bazel.build/" class="external">Bazel</a>
 build system will be required.
 
-Our latest source builds use TensorFlow 2.7.0. To ensure compatibility we use `bazel` version 3.7.2. To remove any existing version of Bazel:
+Our latest source builds use TensorFlow 2.11.0. To ensure compatibility we use `bazel` version 5.1.0. To remove any existing version of Bazel:
 
 <!-- common_typos_disable -->
 <pre class="devsite-click-to-copy">
@@ -92,13 +92,13 @@ Our latest source builds use TensorFlow 2.7.0. To ensure compatibility we use `b
 </pre>
 <!-- common_typos_enable -->
 
-Download and install `bazel` version 3.7.2:
+Download and install `bazel` version 5.1.0:
 
 <!-- common_typos_disable -->
 <pre class="devsite-click-to-copy">
-  <code class="devsite-terminal">wget https://github.com/bazelbuild/bazel/releases/download/3.7.2/bazel_3.7.2-linux-x86_64.deb
+  <code class="devsite-terminal">wget https://github.com/bazelbuild/bazel/releases/download/5.1.0/bazel_5.1.0-linux-x86_64.deb
 </code>
-  <code class="devsite-terminal">sudo dpkg -i bazel_3.7.2-linux-x86_64.deb</code>
+  <code class="devsite-terminal">sudo dpkg -i bazel_5.1.0-linux-x86_64.deb</code>
 </pre>
 <!-- common_typos_enable -->
 
@@ -122,7 +122,7 @@ Finally, confirm installation of the correct `bazel` version:
 ### 4. Build TensorFlow from source
 
 Here we adapt instructions from the TensorFlow [build from source](https://www.tensorflow.org/install/source)
-guide, see the link for further details. TensorFlow Quantum is compatible with TensorFlow version&nbsp;2.7.0.
+guide, see the link for further details. TensorFlow Quantum is compatible with TensorFlow version&nbsp;2.11.0.
 
 Download the
 <a href="https://github.com/tensorflow/tensorflow" class="external">TensorFlow source code</a>:
@@ -131,7 +131,7 @@ Download the
 <pre class="devsite-click-to-copy">
   <code class="devsite-terminal">git clone https://github.com/tensorflow/tensorflow.git</code>
   <code class="devsite-terminal">cd tensorflow</code>
-  <code class="devsite-terminal">git checkout v2.7.0</code>
+  <code class="devsite-terminal">git checkout v2.11.0</code>
 </pre>
 
 Be sure the virtual environment you created in step 2 is activated. Then, install the TensorFlow dependencies:
@@ -142,6 +142,7 @@ Be sure the virtual environment you created in step 2 is activated. Then, instal
   <code class="devsite-terminal">pip install -U keras_applications --no-deps</code>
   <code class="devsite-terminal">pip install -U keras_preprocessing --no-deps</code>
   <code class="devsite-terminal">pip install numpy==1.19.5</code>
+  <code class="devsite-terminal">pip install packaging requests</code>
 </pre>
 <!-- common_typos_enable -->
 
@@ -153,11 +154,11 @@ Configure the TensorFlow build. When asked for the Python interpreter and librar
 </pre>
 <!-- common_typos_enable -->
 
-Build the TensorFlow package:
+Build the TensorFlow package (Since TF v2.8, `_GLIBCXX_USE_CXX11_ABI` is set to 1, and the c++ codes are all compiled with `-std=c++17`):
 
 <!-- common_typos_disable -->
 <pre class="devsite-click-to-copy">
-  <code class="devsite-terminal">bazel build -c opt --cxxopt="-O3" --cxxopt="-march=native" --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" //tensorflow/tools/pip_package:build_pip_package</code>
+  <code class="devsite-terminal">bazel build -c opt --cxxopt="-O3" --cxxopt="-march=native" --cxxopt="-std=c++17" --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=1" //tensorflow/tools/pip_package:build_pip_package</code>
 </pre>
 <!-- common_typos_enable -->
 
@@ -193,7 +194,7 @@ Build the TensorFlow Quantum pip package and install:
 <!-- common_typos_disable -->
 <pre class="devsite-click-to-copy">
   <code class="devsite-terminal">./configure.sh</code>
-  <code class="devsite-terminal">bazel build -c opt --cxxopt="-O3" --cxxopt="-march=native" --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" release:build_pip_package</code>
+  <code class="devsite-terminal">bazel build -c opt --cxxopt="-O3" --cxxopt="-march=native" --cxxopt="-std=c++17" --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=1" release:build_pip_package</code>
   <code class="devsite-terminal">bazel-bin/release/build_pip_package /tmp/tfquantum/</code>
   <code class="devsite-terminal">python3 -m pip install /tmp/tfquantum/<var>name_of_generated_wheel</var>.whl</code>
 </pre>
diff --git a/docs/tutorials/barren_plateaus.ipynb b/docs/tutorials/barren_plateaus.ipynb
index 2447018c9..b35e4f76c 100644
--- a/docs/tutorials/barren_plateaus.ipynb
+++ b/docs/tutorials/barren_plateaus.ipynb
@@ -297,7 +297,7 @@
         "    # Prep the inputs as tensors\n",
         "    circuit_tensor = tfq.convert_to_tensor(circuits)\n",
         "    values_tensor = tf.convert_to_tensor(\n",
-        "        np.random.uniform(0, 2 * np.pi, (n_circuits, 1)).astype(np.float32))\n",
+        "        np.random.uniform(0, 2 * np.pi, (n_circuits, 1)).astype(float))\n",
         "\n",
         "    # Use TensorFlow GradientTape to track gradients.\n",
         "    with tf.GradientTape() as g:\n",
diff --git a/docs/tutorials/gradients.ipynb b/docs/tutorials/gradients.ipynb
index 62a7ce103..2ade263a6 100644
--- a/docs/tutorials/gradients.ipynb
+++ b/docs/tutorials/gradients.ipynb
@@ -370,7 +370,7 @@
       "outputs": [],
       "source": [
         "# Make input_points = [batch_size, 1] array.\n",
-        "input_points = np.linspace(0, 5, 200)[:, np.newaxis].astype(np.float32)\n",
+        "input_points = np.linspace(0, 5, 200)[:, np.newaxis].astype(float)\n",
         "exact_outputs = expectation_calculation(my_circuit,\n",
         "                                        operators=pauli_x,\n",
         "                                        symbol_names=['alpha'],\n",
diff --git a/docs/tutorials/hello_many_worlds.ipynb b/docs/tutorials/hello_many_worlds.ipynb
index f3dd7fe19..75ff512a9 100644
--- a/docs/tutorials/hello_many_worlds.ipynb
+++ b/docs/tutorials/hello_many_worlds.ipynb
@@ -389,7 +389,7 @@
       },
       "outputs": [],
       "source": [
-        "batch_vals = np.array(np.random.uniform(0, 2 * np.pi, (5, 2)), dtype=np.float32)"
+        "batch_vals = np.array(np.random.uniform(0, 2 * np.pi, (5, 2)), dtype=float)"
       ]
     },
     {
@@ -739,10 +739,10 @@
       "outputs": [],
       "source": [
         "# The command input values to the classical NN.\n",
-        "commands = np.array([[0], [1]], dtype=np.float32)\n",
+        "commands = np.array([[0], [1]], dtype=float)\n",
         "\n",
         "# The desired Z expectation value at output of quantum circuit.\n",
-        "expected_outputs = np.array([[1], [-1]], dtype=np.float32)"
+        "expected_outputs = np.array([[1], [-1]], dtype=float)"
       ]
     },
     {
@@ -1108,10 +1108,10 @@
         "operator_data = tfq.convert_to_tensor([[cirq.X(qubit)], [cirq.Z(qubit)]])\n",
         "\n",
         "# The command input values to the classical NN.\n",
-        "commands = np.array([[0], [1]], dtype=np.float32)\n",
+        "commands = np.array([[0], [1]], dtype=float)\n",
         "\n",
         "# The desired expectation value at output of quantum circuit.\n",
-        "expected_outputs = np.array([[1], [-1]], dtype=np.float32)"
+        "expected_outputs = np.array([[1], [-1]], dtype=float)"
       ]
     },
     {
diff --git a/docs/tutorials/mnist.ipynb b/docs/tutorials/mnist.ipynb
index b80f2500f..baed1eac9 100644
--- a/docs/tutorials/mnist.ipynb
+++ b/docs/tutorials/mnist.ipynb
@@ -480,8 +480,8 @@
       "source": [
         "THRESHOLD = 0.5\n",
         "\n",
-        "x_train_bin = np.array(x_train_nocon > THRESHOLD, dtype=np.float32)\n",
-        "x_test_bin = np.array(x_test_small > THRESHOLD, dtype=np.float32)"
+        "x_train_bin = np.array(x_train_nocon > THRESHOLD, dtype=float)\n",
+        "x_test_bin = np.array(x_test_small > THRESHOLD, dtype=float)"
       ]
     },
     {
diff --git a/docs/tutorials/quantum_reinforcement_learning.ipynb b/docs/tutorials/quantum_reinforcement_learning.ipynb
index 50298fe8a..5a59f4080 100644
--- a/docs/tutorials/quantum_reinforcement_learning.ipynb
+++ b/docs/tutorials/quantum_reinforcement_learning.ipynb
@@ -864,7 +864,7 @@
     "    actions = np.concatenate([ep['actions'] for ep in episodes])\n",
     "    rewards = [ep['rewards'] for ep in episodes]\n",
     "    returns = np.concatenate([compute_returns(ep_rwds, gamma) for ep_rwds in rewards])\n",
-    "    returns = np.array(returns, dtype=np.float32)\n",
+    "    returns = np.array(returns, dtype=float)\n",
     "\n",
     "    id_action_pairs = np.array([[i, a] for i, a in enumerate(actions)])\n",
     "    \n",
@@ -1456,9 +1456,9 @@
     "            training_batch = np.random.choice(replay_memory, size=batch_size)\n",
     "            Q_learning_update(np.asarray([x['state'] for x in training_batch]),\n",
     "                              np.asarray([x['action'] for x in training_batch]),\n",
-    "                              np.asarray([x['reward'] for x in training_batch], dtype=np.float32),\n",
+    "                              np.asarray([x['reward'] for x in training_batch], dtype=float),\n",
     "                              np.asarray([x['next_state'] for x in training_batch]),\n",
-    "                              np.asarray([x['done'] for x in training_batch], dtype=np.float32),\n",
+    "                              np.asarray([x['done'] for x in training_batch], dtype=float),\n",
     "                              model, gamma, n_actions)\n",
     "        \n",
     "        # Update target model\n",
diff --git a/release/setup.py b/release/setup.py
index f1a73e717..24424b613 100644
--- a/release/setup.py
+++ b/release/setup.py
@@ -53,13 +53,13 @@ def finalize_options(self):
 REQUIRED_PACKAGES = [
     'cirq-core==0.13.1', 'cirq-google>=0.13.1', 'sympy == 1.8',
     'googleapis-common-protos==1.52.0', 'google-api-core==1.21.0',
-    'google-auth==1.18.0', 'protobuf==3.17.3'
+    'google-auth==1.18.0', 'protobuf==3.19.4'
 ]
 
 # placed as extra to not have required overwrite existing nightly installs if
 # they exist.
-EXTRA_PACKAGES = ['tensorflow == 2.7.0']
-CUR_VERSION = '0.7.2'
+EXTRA_PACKAGES = ['tensorflow == 2.11.0']
+CUR_VERSION = '0.7.3'
 
 
 class BinaryDistribution(Distribution):
diff --git a/requirements.txt b/requirements.txt
index ee7ebc77f..f71ead7d1 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -5,11 +5,11 @@ numpy==1.19.5  # TensorFlow can detect if it was built against other versions.
 nbformat==4.4.0
 pylint==2.4.4
 yapf==0.28.0
-tensorflow==2.7.0
+tensorflow==2.11.0
 # Needed for compatibility with cirq program protos.
 googleapis-common-protos==1.52.0
 google-api-core==1.21.0
 google-auth==1.18.0
 google-api-python-client==1.8.0
 grpcio==1.34.1
-protobuf==3.17.3
+protobuf==3.19.4
diff --git a/scripts/benchmark_all.sh b/scripts/benchmark_all.sh
index 9fbf73387..cd50209c2 100644
--- a/scripts/benchmark_all.sh
+++ b/scripts/benchmark_all.sh
@@ -14,7 +14,7 @@
 # limitations under the License.
 # ==============================================================================
 echo "Testing benchmarks.";
-test_outputs=$(bazel test -c opt --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" --cxxopt="-msse2" --cxxopt="-msse3" --cxxopt="-msse4" --test_output=errors $(bazel query //benchmarks/...))
+test_outputs=$(bazel test -c opt --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=1" --cxxopt="-msse2" --cxxopt="-msse3" --cxxopt="-msse4" --test_output=errors $(bazel query //benchmarks/...))
 exit_code=$?
 
 if [ "$exit_code" == "0" ]; then
@@ -26,5 +26,5 @@ else
 fi
 
 echo "Running preconfigured benchmarks.";
-bazel_run=${bazel run -c opt --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" --cxxopt="-msse2" --cxxopt="-msse3" --cxxopt="-msse4"}
+bazel_run=${bazel run -c opt --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=1" --cxxopt="-msse2" --cxxopt="-msse3" --cxxopt="-msse4"}
 bazel_run benchmarks/scripts:benchmark_clifford_circuit -- --op_density 1 --n_moments 10 --n_qubits 4
\ No newline at end of file
diff --git a/scripts/build_pip_package_test.sh b/scripts/build_pip_package_test.sh
index 1adc7a7e9..644338b6a 100755
--- a/scripts/build_pip_package_test.sh
+++ b/scripts/build_pip_package_test.sh
@@ -19,7 +19,7 @@ pip install -r requirements.txt
 # cd tensorflow_quantum
 echo "Y\n" | ./configure.sh
 
-bazel build -c opt --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" --cxxopt="-msse2" --cxxopt="-msse3" --cxxopt="-msse4" release:build_pip_package
+bazel build -c opt --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=1" --cxxopt="-msse2" --cxxopt="-msse3" --cxxopt="-msse4" release:build_pip_package
 rm /tmp/tensorflow_quantum/* || echo ok
 bazel-bin/release/build_pip_package /tmp/tensorflow_quantum/
 pip install -U /tmp/tensorflow_quantum/*.whl
diff --git a/scripts/msan_test.sh b/scripts/msan_test.sh
index 021b2e9c6..d47e8ccfe 100755
--- a/scripts/msan_test.sh
+++ b/scripts/msan_test.sh
@@ -14,19 +14,19 @@
 # limitations under the License.
 # ==============================================================================
 echo "Testing All Bazel cc_tests with msan.";
-test_outputs=$(bazel test -c opt --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" \
+test_outputs=$(bazel test -c opt --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=1" \
   --cxxopt="-msse2" --cxxopt="-msse3" --cxxopt="-msse4" \
   --cxxopt="-fsanitize=address" --linkopt="-fsanitize=address" \
   --cxxopt="-g" --cxxopt="-O0" \
   --notest_keep_going --test_output=errors \
   //tensorflow_quantum/core/src:all && \
-  bazel test -c opt --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" \
+  bazel test -c opt --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=1" \
   --cxxopt="-mavx2" --cxxopt="-mavx" --cxxopt="-mfma" \
   --cxxopt="-fsanitize=address" --linkopt="-fsanitize=address" \
   --cxxopt="-g" --cxxopt="-O0" \
   --notest_keep_going --test_output=errors \
   //tensorflow_quantum/core/src:all && \
-  bazel test -c opt --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" \
+  bazel test -c opt --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=1" \
   --cxxopt="-fsanitize=address" --linkopt="-fsanitize=address" \
   --cxxopt="-g" --cxxopt="-O0" \
   --notest_keep_going --test_output=errors \
diff --git a/scripts/test_all.sh b/scripts/test_all.sh
index e5513bb12..7a9fc7824 100755
--- a/scripts/test_all.sh
+++ b/scripts/test_all.sh
@@ -14,7 +14,7 @@
 # limitations under the License.
 # ==============================================================================
 echo "Testing All Bazel py_test and cc_tests.";
-test_outputs=$(bazel test -c opt --experimental_repo_remote_exec --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" --cxxopt="-msse2" --cxxopt="-msse3" --cxxopt="-msse4" --notest_keep_going --test_output=errors //tensorflow_quantum/...)
+test_outputs=$(bazel test -c opt --experimental_repo_remote_exec --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=1" --cxxopt="-std=c++17" --cxxopt="-msse2" --cxxopt="-msse3" --cxxopt="-msse4" --notest_keep_going --test_output=errors //tensorflow_quantum/...)
 exit_code=$?
 if [ "$exit_code" == "0" ]; then
 	echo "Testing Complete!";
@@ -23,4 +23,4 @@ else
 	echo "Testing failed, please correct errors before proceeding."
 	echo "{$test_outputs}"
 	exit 64;
-fi
\ No newline at end of file
+fi
diff --git a/scripts/test_benchmarks.sh b/scripts/test_benchmarks.sh
index d969a0d29..07e3adec1 100644
--- a/scripts/test_benchmarks.sh
+++ b/scripts/test_benchmarks.sh
@@ -14,10 +14,10 @@
 # limitations under the License.
 # ==============================================================================
 echo "Testing all Benchmarks.";
-bazel test -c opt --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" --cxxopt="-msse2" --cxxopt="-msse3" --cxxopt="-msse4" --test_output=errors $(bazel query //benchmarks/scripts:all)
-# test_outputs=$(bazel test -c opt --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" --cxxopt="-msse2" --cxxopt="-msse3" --cxxopt="-msse4" --test_output=errors $(bazel query //benchmarks/scripts:all))
+bazel test -c opt --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=1" --cxxopt="-msse2" --cxxopt="-msse3" --cxxopt="-msse4" --test_output=errors $(bazel query //benchmarks/scripts:all)
+# test_outputs=$(bazel test -c opt --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=1" --cxxopt="-msse2" --cxxopt="-msse3" --cxxopt="-msse4" --test_output=errors $(bazel query //benchmarks/scripts:all))
 bench_outputs=$()
-# bench_outputs=$(bazel run -c opt --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" --cxxopt="-msse2" --cxxopt="-msse3" --cxxopt="-msse4" --test_output=errors //benchmarks/scripts:benchmark_clifford_circuit)
+# bench_outputs=$(bazel run -c opt --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=1" --cxxopt="-msse2" --cxxopt="-msse3" --cxxopt="-msse4" --test_output=errors //benchmarks/scripts:benchmark_clifford_circuit)
 exit_code=$?
 if [ "$exit_code" == "0" ]; then
 	echo "Testing Complete!";
diff --git a/tensorflow_quantum/core/ops/BUILD b/tensorflow_quantum/core/ops/BUILD
index 8a534229b..8087a8d3b 100644
--- a/tensorflow_quantum/core/ops/BUILD
+++ b/tensorflow_quantum/core/ops/BUILD
@@ -55,7 +55,7 @@ cc_binary(
             "-DNOGDI",
             "/d2ReducedOptimizeHugeFunctions",
             "/arch:AVX",
-            "/std:c++14",
+            "/std:c++17",
             "-DTENSORFLOW_MONOLITHIC_BUILD",
             "/DPLATFORM_WINDOWS",
             "/DEIGEN_HAS_C99_MATH",
@@ -69,8 +69,8 @@ cc_binary(
         ],
         "//conditions:default": [
             "-pthread",
-            "-std=c++14",
-            "-D_GLIBCXX_USE_CXX11_ABI=0",
+            "-std=c++17",
+            "-D_GLIBCXX_USE_CXX11_ABI=1",
         ],
     }),
     features = select({
@@ -118,7 +118,7 @@ cc_binary(
             "-DNOGDI",
             "/d2ReducedOptimizeHugeFunctions",
             "/arch:AVX",
-            "/std:c++14",
+            "/std:c++17",
             "-DTENSORFLOW_MONOLITHIC_BUILD",
             "/DPLATFORM_WINDOWS",
             "/DEIGEN_HAS_C99_MATH",
@@ -132,8 +132,8 @@ cc_binary(
         ],
         "//conditions:default": [
             "-pthread",
-            "-std=c++14",
-            "-D_GLIBCXX_USE_CXX11_ABI=0",
+            "-std=c++17",
+            "-D_GLIBCXX_USE_CXX11_ABI=1",
         ],
     }),
     features = select({
@@ -176,7 +176,7 @@ cc_binary(
             "-DNOGDI",
             "/d2ReducedOptimizeHugeFunctions",
             "/arch:AVX",
-            "/std:c++14",
+            "/std:c++17",
             "-DTENSORFLOW_MONOLITHIC_BUILD",
             "/DPLATFORM_WINDOWS",
             "/DEIGEN_HAS_C99_MATH",
@@ -190,8 +190,8 @@ cc_binary(
         ],
         "//conditions:default": [
             "-pthread",
-            "-std=c++14",
-            "-D_GLIBCXX_USE_CXX11_ABI=0",
+            "-std=c++17",
+            "-D_GLIBCXX_USE_CXX11_ABI=1",
         ],
     }),
     features = select({
@@ -242,7 +242,7 @@ cc_binary(
             "-DNOGDI",
             "/d2ReducedOptimizeHugeFunctions",
             "/arch:AVX",
-            "/std:c++14",
+            "/std:c++17",
             "-DTENSORFLOW_MONOLITHIC_BUILD",
             "/DPLATFORM_WINDOWS",
             "/DEIGEN_HAS_C99_MATH",
@@ -256,8 +256,8 @@ cc_binary(
         ],
         "//conditions:default": [
             "-pthread",
-            "-std=c++14",
-            "-D_GLIBCXX_USE_CXX11_ABI=0",
+            "-std=c++17",
+            "-D_GLIBCXX_USE_CXX11_ABI=1",
         ],
     }),
     features = select({
@@ -301,7 +301,7 @@ cc_library(
             "-DNOGDI",
             "/d2ReducedOptimizeHugeFunctions",
             "/arch:AVX",
-            "/std:c++14",
+            "/std:c++17",
             "-DTENSORFLOW_MONOLITHIC_BUILD",
             "/DPLATFORM_WINDOWS",
             "/DEIGEN_HAS_C99_MATH",
@@ -315,8 +315,8 @@ cc_library(
         ],
         "//conditions:default": [
             "-pthread",
-            "-std=c++14",
-            "-D_GLIBCXX_USE_CXX11_ABI=0",
+            "-std=c++17",
+            "-D_GLIBCXX_USE_CXX11_ABI=1",
         ],
     }),
     deps = [
@@ -354,7 +354,7 @@ cc_binary(
             "-DNOGDI",
             "/d2ReducedOptimizeHugeFunctions",
             "/arch:AVX",
-            "/std:c++14",
+            "/std:c++17",
             "-DTENSORFLOW_MONOLITHIC_BUILD",
             "/DPLATFORM_WINDOWS",
             "/DEIGEN_HAS_C99_MATH",
@@ -368,8 +368,8 @@ cc_binary(
         ],
         "//conditions:default": [
             "-pthread",
-            "-std=c++14",
-            "-D_GLIBCXX_USE_CXX11_ABI=0",
+            "-std=c++17",
+            "-D_GLIBCXX_USE_CXX11_ABI=1",
         ],
     }),
     features = select({
@@ -416,7 +416,7 @@ cc_library(
             "-DNOGDI",
             "/d2ReducedOptimizeHugeFunctions",
             "/arch:AVX",
-            "/std:c++14",
+            "/std:c++17",
             "-DTENSORFLOW_MONOLITHIC_BUILD",
             "/DPLATFORM_WINDOWS",
             "/DEIGEN_HAS_C99_MATH",
@@ -430,8 +430,8 @@ cc_library(
         ],
         "//conditions:default": [
             "-pthread",
-            "-std=c++14",
-            "-D_GLIBCXX_USE_CXX11_ABI=0",
+            "-std=c++17",
+            "-D_GLIBCXX_USE_CXX11_ABI=1",
         ],
     }),
     deps = [
diff --git a/tensorflow_quantum/core/ops/cirq_ops_test.py b/tensorflow_quantum/core/ops/cirq_ops_test.py
index ffbfab1de..1b54771a9 100644
--- a/tensorflow_quantum/core/ops/cirq_ops_test.py
+++ b/tensorflow_quantum/core/ops/cirq_ops_test.py
@@ -457,7 +457,7 @@ def run_sweep(self, program, params, repetitions):
                             'tfq':
                                 np.array([[1] * len(program.all_qubits())] *
                                          repetitions,
-                                         dtype=np.int32),
+                                         dtype=int),
                         }) for param in cirq.to_resolvers(params)
                 ]
 
diff --git a/tensorflow_quantum/core/ops/math_ops/BUILD b/tensorflow_quantum/core/ops/math_ops/BUILD
index 51bad72fb..6eb8a0320 100644
--- a/tensorflow_quantum/core/ops/math_ops/BUILD
+++ b/tensorflow_quantum/core/ops/math_ops/BUILD
@@ -38,7 +38,7 @@ cc_binary(
             "-DNOGDI",
             "/d2ReducedOptimizeHugeFunctions",
             "/arch:AVX",
-            "/std:c++14",
+            "/std:c++17",
             "-DTENSORFLOW_MONOLITHIC_BUILD",
             "/DPLATFORM_WINDOWS",
             "/DEIGEN_HAS_C99_MATH",
@@ -52,8 +52,8 @@ cc_binary(
         ],
         "//conditions:default": [
             "-pthread",
-            "-std=c++14",
-            "-D_GLIBCXX_USE_CXX11_ABI=0",
+            "-std=c++17",
+            "-D_GLIBCXX_USE_CXX11_ABI=1",
         ],
     }),
     features = select({
diff --git a/tensorflow_quantum/core/ops/math_ops/simulate_mps_test.py b/tensorflow_quantum/core/ops/math_ops/simulate_mps_test.py
index 1120b1392..ee7317b6a 100644
--- a/tensorflow_quantum/core/ops/math_ops/simulate_mps_test.py
+++ b/tensorflow_quantum/core/ops/math_ops/simulate_mps_test.py
@@ -934,7 +934,10 @@ def test_complex_equality(self):
         ] for _ in range(batch_size)]
         symbol_names = []
         resolver_batch = [{} for _ in range(batch_size)]
-        num_samples = np.ones_like(pauli_sums, dtype=np.int32) * 1000
+        # Because `pauli_sums` has inhomogeneous shape due to the different
+        # number of terms, `np.ones_like` failed with `pauli_sums`.
+        puali_sums_len = [[len(x) for x in y] for y in pauli_sums]
+        num_samples = np.ones_like(puali_sums_len, dtype=int) * 1000
 
         symbol_values_array = np.array(
             [[resolver[symbol]
diff --git a/tensorflow_quantum/core/ops/math_ops/tfq_inner_product.cc b/tensorflow_quantum/core/ops/math_ops/tfq_inner_product.cc
index 2a66d2919..74751f9cc 100644
--- a/tensorflow_quantum/core/ops/math_ops/tfq_inner_product.cc
+++ b/tensorflow_quantum/core/ops/math_ops/tfq_inner_product.cc
@@ -87,7 +87,7 @@ class TfqInnerProductOp : public tensorflow::OpKernel {
     std::vector<QsimFusedCircuit> fused_circuits(programs.size(),
                                                  QsimFusedCircuit({}));
 
-    Status parse_status = Status::OK();
+    Status parse_status = ::tensorflow::Status();
     auto p_lock = tensorflow::mutex();
     auto construct_f = [&](int start, int end) {
       for (int i = start; i < end; i++) {
@@ -314,7 +314,7 @@ REGISTER_OP("TfqInnerProduct")
           c->Dim(other_programs_shape, 1);
       c->set_output(0, c->Matrix(output_rows, output_cols));
 
-      return tensorflow::Status::OK();
+      return ::tensorflow::Status();
     });
 
 }  // namespace tfq
diff --git a/tensorflow_quantum/core/ops/math_ops/tfq_inner_product_grad.cc b/tensorflow_quantum/core/ops/math_ops/tfq_inner_product_grad.cc
index 5b29571d2..3db493b11 100644
--- a/tensorflow_quantum/core/ops/math_ops/tfq_inner_product_grad.cc
+++ b/tensorflow_quantum/core/ops/math_ops/tfq_inner_product_grad.cc
@@ -112,7 +112,7 @@ class TfqInnerProductGradOp : public tensorflow::OpKernel {
     std::vector<std::vector<GradientOfGate>> gradient_gates(
         programs.size(), std::vector<GradientOfGate>({}));
 
-    Status parse_status = Status::OK();
+    Status parse_status = ::tensorflow::Status();
     auto p_lock = tensorflow::mutex();
     auto construct_f = [&](int start, int end) {
       for (int i = start; i < end; i++) {
@@ -490,7 +490,7 @@ REGISTER_OP("TfqInnerProductGrad")
                  {output_rows,
                   tensorflow::shape_inference::InferenceContext::kUnknownDim}));
 
-      return tensorflow::Status::OK();
+      return ::tensorflow::Status();
     });
 
 }  // namespace tfq
diff --git a/tensorflow_quantum/core/ops/math_ops/tfq_simulate_1d_expectation.cc b/tensorflow_quantum/core/ops/math_ops/tfq_simulate_1d_expectation.cc
index adb1d9bb6..c00b43a9b 100644
--- a/tensorflow_quantum/core/ops/math_ops/tfq_simulate_1d_expectation.cc
+++ b/tensorflow_quantum/core/ops/math_ops/tfq_simulate_1d_expectation.cc
@@ -100,7 +100,7 @@ class TfqSimulateMPS1DExpectationOp : public tensorflow::OpKernel {
     std::vector<QsimCircuit> qsim_circuits(programs.size(), QsimCircuit());
     std::vector<QsimFusedCircuit> fused_circuits(programs.size(),
                                                  QsimFusedCircuit({}));
-    Status parse_status = Status::OK();
+    Status parse_status = ::tensorflow::Status();
     auto p_lock = tensorflow::mutex();
     auto construct_f = [&](int start, int end) {
       for (int i = start; i < end; i++) {
@@ -153,7 +153,7 @@ class TfqSimulateMPS1DExpectationOp : public tensorflow::OpKernel {
 
     const int output_dim_op_size = output_tensor->dimension(1);
 
-    Status compute_status = Status::OK();
+    Status compute_status = ::tensorflow::Status();
     auto c_lock = tensorflow::mutex();
     auto DoWork = [&](int start, int end) {
       int old_batch_index = -2;
@@ -247,7 +247,7 @@ REGISTER_OP("TfqSimulateMPS1DExpectation")
           c->Dim(pauli_sums_shape, 1);
       c->set_output(0, c->Matrix(output_rows, output_cols));
 
-      return tensorflow::Status::OK();
+      return ::tensorflow::Status();
     });
 
 }  // namespace tfq
diff --git a/tensorflow_quantum/core/ops/math_ops/tfq_simulate_1d_sampled_expectation.cc b/tensorflow_quantum/core/ops/math_ops/tfq_simulate_1d_sampled_expectation.cc
index 750531f16..ba94e8c72 100644
--- a/tensorflow_quantum/core/ops/math_ops/tfq_simulate_1d_sampled_expectation.cc
+++ b/tensorflow_quantum/core/ops/math_ops/tfq_simulate_1d_sampled_expectation.cc
@@ -113,7 +113,7 @@ class TfqSimulateMPS1DSampledExpectationOp : public tensorflow::OpKernel {
     std::vector<std::vector<qsim::GateFused<QsimGate>>> fused_circuits(
         programs.size(), std::vector<qsim::GateFused<QsimGate>>({}));
 
-    Status parse_status = Status::OK();
+    Status parse_status = ::tensorflow::Status();
     auto p_lock = tensorflow::mutex();
     auto construct_f = [&](int start, int end) {
       for (int i = start; i < end; i++) {
@@ -179,7 +179,7 @@ class TfqSimulateMPS1DSampledExpectationOp : public tensorflow::OpKernel {
                                 ->tensorflow_cpu_worker_threads()
                                 ->workers->NumThreads();
 
-    Status compute_status = Status::OK();
+    Status compute_status = ::tensorflow::Status();
     auto c_lock = tensorflow::mutex();
     auto DoWork = [&](int start, int end) {
       int old_batch_index = -2;
@@ -291,7 +291,7 @@ REGISTER_OP("TfqSimulateMPS1DSampledExpectation")
           c->Dim(pauli_sums_shape, 1);
       c->set_output(0, c->Matrix(output_rows, output_cols));
 
-      return tensorflow::Status::OK();
+      return ::tensorflow::Status();
     });
 
 }  // namespace tfq
diff --git a/tensorflow_quantum/core/ops/math_ops/tfq_simulate_1d_samples.cc b/tensorflow_quantum/core/ops/math_ops/tfq_simulate_1d_samples.cc
index 495e5f8f2..608f94689 100644
--- a/tensorflow_quantum/core/ops/math_ops/tfq_simulate_1d_samples.cc
+++ b/tensorflow_quantum/core/ops/math_ops/tfq_simulate_1d_samples.cc
@@ -85,7 +85,7 @@ class TfqSimulateMPS1DSamplesOp : public tensorflow::OpKernel {
     std::vector<std::vector<qsim::GateFused<QsimGate>>> fused_circuits(
         programs.size(), std::vector<qsim::GateFused<QsimGate>>({}));
 
-    Status parse_status = Status::OK();
+    Status parse_status = ::tensorflow::Status();
     auto p_lock = tensorflow::mutex();
     auto construct_f = [&](int start, int end) {
       for (int i = start; i < end; i++) {
@@ -242,7 +242,7 @@ REGISTER_OP("TfqSimulateMPS1DSamples")
                   tensorflow::shape_inference::InferenceContext::kUnknownDim,
                   tensorflow::shape_inference::InferenceContext::kUnknownDim}));
 
-      return tensorflow::Status::OK();
+      return ::tensorflow::Status();
     });
 
 }  // namespace tfq
diff --git a/tensorflow_quantum/core/ops/noise/BUILD b/tensorflow_quantum/core/ops/noise/BUILD
index a72665780..3758037e5 100644
--- a/tensorflow_quantum/core/ops/noise/BUILD
+++ b/tensorflow_quantum/core/ops/noise/BUILD
@@ -36,7 +36,7 @@ cc_binary(
             "-DNOGDI",
             "/d2ReducedOptimizeHugeFunctions",
             "/arch:AVX",
-            "/std:c++14",
+            "/std:c++17",
             "-DTENSORFLOW_MONOLITHIC_BUILD",
             "/DPLATFORM_WINDOWS",
             "/DEIGEN_HAS_C99_MATH",
@@ -50,8 +50,8 @@ cc_binary(
         ],
         "//conditions:default": [
             "-pthread",
-            "-std=c++14",
-            "-D_GLIBCXX_USE_CXX11_ABI=0",
+            "-std=c++17",
+            "-D_GLIBCXX_USE_CXX11_ABI=1",
         ],
     }),
     features = select({
diff --git a/tensorflow_quantum/core/ops/noise/tfq_noisy_expectation.cc b/tensorflow_quantum/core/ops/noise/tfq_noisy_expectation.cc
index 430b8d303..c67fa01f7 100644
--- a/tensorflow_quantum/core/ops/noise/tfq_noisy_expectation.cc
+++ b/tensorflow_quantum/core/ops/noise/tfq_noisy_expectation.cc
@@ -111,7 +111,7 @@ class TfqNoisyExpectationOp : public tensorflow::OpKernel {
     std::vector<NoisyQsimCircuit> qsim_circuits(programs.size(),
                                                 NoisyQsimCircuit());
 
-    Status parse_status = Status::OK();
+    Status parse_status = ::tensorflow::Status();
     auto p_lock = tensorflow::mutex();
     auto construct_f = [&](int start, int end) {
       for (int i = start; i < end; i++) {
@@ -287,7 +287,7 @@ class TfqNoisyExpectationOp : public tensorflow::OpKernel {
     }
     random_gen.Init(tensorflow::random::New64(), tensorflow::random::New64());
 
-    Status compute_status = Status::OK();
+    Status compute_status = ::tensorflow::Status();
     auto c_lock = tensorflow::mutex();
     auto DoWork = [&](int start, int end) {
       // Begin simulation.
@@ -418,7 +418,7 @@ REGISTER_OP("TfqNoisyExpectation")
           c->Dim(pauli_sums_shape, 1);
       c->set_output(0, c->Matrix(output_rows, output_cols));
 
-      return tensorflow::Status::OK();
+      return ::tensorflow::Status();
     });
 
 }  // namespace tfq
diff --git a/tensorflow_quantum/core/ops/noise/tfq_noisy_sampled_expectation.cc b/tensorflow_quantum/core/ops/noise/tfq_noisy_sampled_expectation.cc
index c6b66d861..aa0c85691 100644
--- a/tensorflow_quantum/core/ops/noise/tfq_noisy_sampled_expectation.cc
+++ b/tensorflow_quantum/core/ops/noise/tfq_noisy_sampled_expectation.cc
@@ -112,7 +112,7 @@ class TfqNoisySampledExpectationOp : public tensorflow::OpKernel {
     std::vector<NoisyQsimCircuit> qsim_circuits(programs.size(),
                                                 NoisyQsimCircuit());
 
-    Status parse_status = Status::OK();
+    Status parse_status = ::tensorflow::Status();
     auto p_lock = tensorflow::mutex();
     auto construct_f = [&](int start, int end) {
       for (int i = start; i < end; i++) {
@@ -294,7 +294,7 @@ class TfqNoisySampledExpectationOp : public tensorflow::OpKernel {
     }
     random_gen.Init(tensorflow::random::New64(), tensorflow::random::New64());
 
-    Status compute_status = Status::OK();
+    Status compute_status = ::tensorflow::Status();
     auto c_lock = tensorflow::mutex();
     auto DoWork = [&](int start, int end) {
       // Begin simulation.
@@ -424,7 +424,7 @@ REGISTER_OP("TfqNoisySampledExpectation")
           c->Dim(pauli_sums_shape, 1);
       c->set_output(0, c->Matrix(output_rows, output_cols));
 
-      return tensorflow::Status::OK();
+      return ::tensorflow::Status();
     });
 
 }  // namespace tfq
diff --git a/tensorflow_quantum/core/ops/noise/tfq_noisy_samples.cc b/tensorflow_quantum/core/ops/noise/tfq_noisy_samples.cc
index f8a102914..341c87910 100644
--- a/tensorflow_quantum/core/ops/noise/tfq_noisy_samples.cc
+++ b/tensorflow_quantum/core/ops/noise/tfq_noisy_samples.cc
@@ -82,7 +82,7 @@ class TfqNoisySamplesOp : public tensorflow::OpKernel {
     std::vector<NoisyQsimCircuit> qsim_circuits(programs.size(),
                                                 NoisyQsimCircuit());
 
-    Status parse_status = Status::OK();
+    Status parse_status = ::tensorflow::Status();
     auto p_lock = tensorflow::mutex();
     auto construct_f = [&](int start, int end) {
       for (int i = start; i < end; i++) {
@@ -343,7 +343,7 @@ REGISTER_OP("TfqNoisySamples")
                   tensorflow::shape_inference::InferenceContext::kUnknownDim,
                   tensorflow::shape_inference::InferenceContext::kUnknownDim}));
 
-      return tensorflow::Status::OK();
+      return ::tensorflow::Status();
     });
 
 }  // namespace tfq
diff --git a/tensorflow_quantum/core/ops/parse_context.cc b/tensorflow_quantum/core/ops/parse_context.cc
index dd8f941fc..f926d15bb 100644
--- a/tensorflow_quantum/core/ops/parse_context.cc
+++ b/tensorflow_quantum/core/ops/parse_context.cc
@@ -42,16 +42,17 @@ template <typename T>
 Status ParseProto(const std::string& text, T* proto) {
   // First attempt to parse from the binary representation.
   if (proto->ParseFromString(text)) {
-    return Status::OK();
+    return ::tensorflow::Status();
   }
 
   // If that fails, then try to parse from the human readable representation.
   if (google::protobuf::TextFormat::ParseFromString(text, proto)) {
-    return Status::OK();
+    return ::tensorflow::Status();
   }
 
-  return Status(tensorflow::error::INVALID_ARGUMENT,
-                "Unparseable proto: " + text);
+  return Status(
+      static_cast<tensorflow::errors::Code>(absl::StatusCode::kInvalidArgument),
+      "Unparseable proto: " + text);
 }
 
 }  // namespace
@@ -67,7 +68,8 @@ Status ParsePrograms(OpKernelContext* context, const std::string& input_name,
   if (input->dims() != 1) {
     // Never parse anything other than a 1d list of circuits.
     return Status(
-        tensorflow::error::INVALID_ARGUMENT,
+        static_cast<tensorflow::errors::Code>(
+            absl::StatusCode::kInvalidArgument),
         absl::StrCat("programs must be rank 1. Got rank ", input->dims(), "."));
   }
 
@@ -86,7 +88,7 @@ Status ParsePrograms(OpKernelContext* context, const std::string& input_name,
   context->device()->tensorflow_cpu_worker_threads()->workers->ParallelFor(
       num_programs, cycle_estimate, DoWork);
 
-  return Status::OK();
+  return ::tensorflow::Status();
 }
 
 Status ParsePrograms2D(OpKernelContext* context, const std::string& input_name,
@@ -99,7 +101,8 @@ Status ParsePrograms2D(OpKernelContext* context, const std::string& input_name,
 
   if (input->dims() != 2) {
     // Never parse anything other than a 1d list of circuits.
-    return Status(tensorflow::error::INVALID_ARGUMENT,
+    return Status(static_cast<tensorflow::errors::Code>(
+                      absl::StatusCode::kInvalidArgument),
                   absl::StrCat("other_programs must be rank 2. Got rank ",
                                input->dims(), "."));
   }
@@ -123,7 +126,7 @@ Status ParsePrograms2D(OpKernelContext* context, const std::string& input_name,
   context->device()->tensorflow_cpu_worker_threads()->workers->ParallelFor(
       num_programs * num_entries, cycle_estimate, DoWork);
 
-  return Status::OK();
+  return ::tensorflow::Status();
 }
 
 Status GetProgramsAndProgramsToAppend(
@@ -140,11 +143,12 @@ Status GetProgramsAndProgramsToAppend(
   }
 
   if (programs->size() != programs_to_append->size()) {
-    return Status(tensorflow::error::INVALID_ARGUMENT,
+    return Status(static_cast<tensorflow::errors::Code>(
+                      absl::StatusCode::kInvalidArgument),
                   "programs and programs_to_append must have matching sizes.");
   }
 
-  return Status::OK();
+  return ::tensorflow::Status();
 }
 
 Status GetProgramsAndNumQubits(
@@ -167,7 +171,8 @@ Status GetProgramsAndNumQubits(
     }
     if (programs->size() != p_sums->size()) {
       return Status(
-          tensorflow::error::INVALID_ARGUMENT,
+          static_cast<tensorflow::errors::Code>(
+              absl::StatusCode::kInvalidArgument),
           absl::StrCat("Number of circuits and PauliSums do not match. Got ",
                        programs->size(), " circuits and ", p_sums->size(),
                        " paulisums."));
@@ -197,7 +202,7 @@ Status GetProgramsAndNumQubits(
   context->device()->tensorflow_cpu_worker_threads()->workers->ParallelFor(
       num_qubits->size(), cycle_estimate, DoWork);
 
-  return Status::OK();
+  return ::tensorflow::Status();
 }
 
 tensorflow::Status GetProgramsAndNumQubits(
@@ -218,7 +223,8 @@ tensorflow::Status GetProgramsAndNumQubits(
   }
 
   if (programs->size() != other_programs->size()) {
-    return Status(tensorflow::error::INVALID_ARGUMENT,
+    return Status(static_cast<tensorflow::errors::Code>(
+                      absl::StatusCode::kInvalidArgument),
                   absl::StrCat("programs and other_programs batch dimension",
                                " do not match. Foud: ", programs->size(),
                                " and ", other_programs->size()));
@@ -241,7 +247,7 @@ tensorflow::Status GetProgramsAndNumQubits(
   context->device()->tensorflow_cpu_worker_threads()->workers->ParallelFor(
       num_qubits->size(), cycle_estimate, DoWork);
 
-  return Status::OK();
+  return ::tensorflow::Status();
 }
 
 Status GetPauliSums(OpKernelContext* context,
@@ -254,7 +260,8 @@ Status GetPauliSums(OpKernelContext* context,
   }
 
   if (input->dims() != 2) {
-    return Status(tensorflow::error::INVALID_ARGUMENT,
+    return Status(static_cast<tensorflow::errors::Code>(
+                      absl::StatusCode::kInvalidArgument),
                   absl::StrCat("pauli_sums must be rank 2. Got rank ",
                                input->dims(), "."));
   }
@@ -278,7 +285,7 @@ Status GetPauliSums(OpKernelContext* context,
   context->device()->tensorflow_cpu_worker_threads()->workers->ParallelFor(
       sum_specs.dimension(0) * sum_specs.dimension(1), cycle_estimate, DoWork);
 
-  return Status::OK();
+  return ::tensorflow::Status();
 }
 
 Status GetSymbolMaps(OpKernelContext* context, std::vector<SymbolMap>* maps) {
@@ -290,7 +297,8 @@ Status GetSymbolMaps(OpKernelContext* context, std::vector<SymbolMap>* maps) {
   }
 
   if (input_names->dims() != 1) {
-    return Status(tensorflow::error::INVALID_ARGUMENT,
+    return Status(static_cast<tensorflow::errors::Code>(
+                      absl::StatusCode::kInvalidArgument),
                   absl::StrCat("symbol_names must be rank 1. Got rank ",
                                input_names->dims(), "."));
   }
@@ -302,7 +310,8 @@ Status GetSymbolMaps(OpKernelContext* context, std::vector<SymbolMap>* maps) {
   }
 
   if (input_values->dims() != 2) {
-    return Status(tensorflow::error::INVALID_ARGUMENT,
+    return Status(static_cast<tensorflow::errors::Code>(
+                      absl::StatusCode::kInvalidArgument),
                   absl::StrCat("symbol_values must be rank 2. Got rank ",
                                input_values->dims(), "."));
   }
@@ -311,7 +320,8 @@ Status GetSymbolMaps(OpKernelContext* context, std::vector<SymbolMap>* maps) {
   const auto symbol_values = input_values->matrix<float>();
 
   if (symbol_names.dimension(0) != symbol_values.dimension(1)) {
-    return Status(tensorflow::error::INVALID_ARGUMENT,
+    return Status(static_cast<tensorflow::errors::Code>(
+                      absl::StatusCode::kInvalidArgument),
                   "Input symbol names and value sizes do not match.");
   }
 
@@ -333,7 +343,7 @@ Status GetSymbolMaps(OpKernelContext* context, std::vector<SymbolMap>* maps) {
   context->device()->tensorflow_cpu_worker_threads()->workers->ParallelFor(
       symbol_values.dimension(0), cycle_estimate, DoWork);
 
-  return Status::OK();
+  return ::tensorflow::Status();
 }
 
 tensorflow::Status GetNumSamples(
@@ -346,7 +356,8 @@ tensorflow::Status GetNumSamples(
   }
 
   if (input_num_samples->dims() != 2) {
-    return Status(tensorflow::error::INVALID_ARGUMENT,
+    return Status(static_cast<tensorflow::errors::Code>(
+                      absl::StatusCode::kInvalidArgument),
                   absl::StrCat("num_samples must be rank 2. Got rank ",
                                input_num_samples->dims(), "."));
   }
@@ -359,7 +370,8 @@ tensorflow::Status GetNumSamples(
     for (unsigned int j = 0; j < matrix_num_samples.dimension(1); j++) {
       const int num_samples = matrix_num_samples(i, j);
       if (num_samples < 1) {
-        return Status(tensorflow::error::INVALID_ARGUMENT,
+        return Status(static_cast<tensorflow::errors::Code>(
+                          absl::StatusCode::kInvalidArgument),
                       "Each element of num_samples must be greater than 0.");
       }
       sub_parsed_num_samples.push_back(num_samples);
@@ -367,7 +379,7 @@ tensorflow::Status GetNumSamples(
     parsed_num_samples->push_back(sub_parsed_num_samples);
   }
 
-  return Status::OK();
+  return ::tensorflow::Status();
 }
 
 // used by tfq_simulate_samples.
@@ -380,7 +392,8 @@ Status GetIndividualSample(tensorflow::OpKernelContext* context,
   }
 
   if (input_num_samples->dims() != 1) {
-    return Status(tensorflow::error::INVALID_ARGUMENT,
+    return Status(static_cast<tensorflow::errors::Code>(
+                      absl::StatusCode::kInvalidArgument),
                   absl::StrCat("num_samples must be rank 1. Got rank ",
                                input_num_samples->dims(), "."));
   }
@@ -388,13 +401,14 @@ Status GetIndividualSample(tensorflow::OpKernelContext* context,
   const auto vector_num_samples = input_num_samples->vec<int>();
 
   if (vector_num_samples.dimension(0) != 1) {
-    return Status(tensorflow::error::INVALID_ARGUMENT,
+    return Status(static_cast<tensorflow::errors::Code>(
+                      absl::StatusCode::kInvalidArgument),
                   absl::StrCat("num_samples must contain 1 element. Got ",
                                vector_num_samples.dimension(0), "."));
   }
 
   (*n_samples) = vector_num_samples(0);
-  return Status::OK();
+  return ::tensorflow::Status();
 }
 
 // used by adj_grad_op.
@@ -408,7 +422,8 @@ tensorflow::Status GetPrevGrads(
   }
 
   if (input_grads->dims() != 2) {
-    return Status(tensorflow::error::INVALID_ARGUMENT,
+    return Status(static_cast<tensorflow::errors::Code>(
+                      absl::StatusCode::kInvalidArgument),
                   absl::StrCat("downstream_grads must be rank 2. Got rank ",
                                input_grads->dims(), "."));
   }
@@ -425,7 +440,7 @@ tensorflow::Status GetPrevGrads(
     parsed_prev_grads->push_back(sub_parsed_grads);
   }
 
-  return Status::OK();
+  return ::tensorflow::Status();
 }
 
 }  // namespace tfq
diff --git a/tensorflow_quantum/core/ops/tfq_adj_grad_op.cc b/tensorflow_quantum/core/ops/tfq_adj_grad_op.cc
index 7625c7962..e7252baee 100644
--- a/tensorflow_quantum/core/ops/tfq_adj_grad_op.cc
+++ b/tensorflow_quantum/core/ops/tfq_adj_grad_op.cc
@@ -99,7 +99,7 @@ class TfqAdjointGradientOp : public tensorflow::OpKernel {
     std::vector<std::vector<GradientOfGate>> gradient_gates(
         programs.size(), std::vector<GradientOfGate>({}));
 
-    Status parse_status = Status::OK();
+    Status parse_status = ::tensorflow::Status();
     auto p_lock = tensorflow::mutex();
     auto construct_f = [&](int start, int end) {
       for (int i = start; i < end; i++) {
@@ -411,7 +411,7 @@ REGISTER_OP("TfqAdjointGradient")
           c->Dim(symbol_names_shape, 0);
       c->set_output(0, c->Matrix(output_rows, output_cols));
 
-      return tensorflow::Status::OK();
+      return ::tensorflow::Status();
     });
 
 }  // namespace tfq
diff --git a/tensorflow_quantum/core/ops/tfq_calculate_unitary_op.cc b/tensorflow_quantum/core/ops/tfq_calculate_unitary_op.cc
index b06a7faef..ace5327e1 100644
--- a/tensorflow_quantum/core/ops/tfq_calculate_unitary_op.cc
+++ b/tensorflow_quantum/core/ops/tfq_calculate_unitary_op.cc
@@ -68,7 +68,7 @@ class TfqCalculateUnitaryOp : public tensorflow::OpKernel {
     std::vector<std::vector<qsim::GateFused<QsimGate>>> fused_circuits(
         programs.size(), std::vector<qsim::GateFused<QsimGate>>({}));
 
-    Status parse_status = Status::OK();
+    Status parse_status = ::tensorflow::Status();
     auto p_lock = tensorflow::mutex();
     auto construct_f = [&](int start, int end) {
       for (int i = start; i < end; i++) {
@@ -182,7 +182,7 @@ REGISTER_OP("TfqCalculateUnitary")
                   tensorflow::shape_inference::InferenceContext::kUnknownDim,
                   tensorflow::shape_inference::InferenceContext::kUnknownDim}));
 
-      return tensorflow::Status::OK();
+      return ::tensorflow::Status();
     });
 
 }  // namespace tfq
diff --git a/tensorflow_quantum/core/ops/tfq_circuit_append_op.cc b/tensorflow_quantum/core/ops/tfq_circuit_append_op.cc
index 8109ccd56..582bd1681 100644
--- a/tensorflow_quantum/core/ops/tfq_circuit_append_op.cc
+++ b/tensorflow_quantum/core/ops/tfq_circuit_append_op.cc
@@ -90,7 +90,7 @@ REGISTER_OP("TfqAppendCircuit")
 
       c->set_output(0, c->input(0));
 
-      return tensorflow::Status::OK();
+      return ::tensorflow::Status();
     });
 
 }  // namespace tfq
diff --git a/tensorflow_quantum/core/ops/tfq_ps_decompose_op.cc b/tensorflow_quantum/core/ops/tfq_ps_decompose_op.cc
index f4355c07c..669ea6368 100644
--- a/tensorflow_quantum/core/ops/tfq_ps_decompose_op.cc
+++ b/tensorflow_quantum/core/ops/tfq_ps_decompose_op.cc
@@ -326,7 +326,7 @@ REGISTER_OP("TfqPsDecompose")
 
       c->set_output(0, c->input(0));
 
-      return tensorflow::Status::OK();
+      return ::tensorflow::Status();
     });
 
 }  // namespace tfq
diff --git a/tensorflow_quantum/core/ops/tfq_ps_symbol_replace_op.cc b/tensorflow_quantum/core/ops/tfq_ps_symbol_replace_op.cc
index 3423177b0..559fbecc9 100644
--- a/tensorflow_quantum/core/ops/tfq_ps_symbol_replace_op.cc
+++ b/tensorflow_quantum/core/ops/tfq_ps_symbol_replace_op.cc
@@ -206,7 +206,7 @@ REGISTER_OP("TfqPsSymbolReplace")
                   tensorflow::shape_inference::InferenceContext::kUnknownDim,
                   tensorflow::shape_inference::InferenceContext::kUnknownDim}));
 
-      return tensorflow::Status::OK();
+      return ::tensorflow::Status();
     });
 
 }  // namespace tfq
diff --git a/tensorflow_quantum/core/ops/tfq_ps_weights_from_symbols_op.cc b/tensorflow_quantum/core/ops/tfq_ps_weights_from_symbols_op.cc
index 94aaf6641..4a027223e 100644
--- a/tensorflow_quantum/core/ops/tfq_ps_weights_from_symbols_op.cc
+++ b/tensorflow_quantum/core/ops/tfq_ps_weights_from_symbols_op.cc
@@ -184,7 +184,7 @@ REGISTER_OP("TfqPsWeightsFromSymbols")
                   tensorflow::shape_inference::InferenceContext::kUnknownDim,
                   tensorflow::shape_inference::InferenceContext::kUnknownDim}));
 
-      return tensorflow::Status::OK();
+      return ::tensorflow::Status();
     });
 
 }  // namespace tfq
diff --git a/tensorflow_quantum/core/ops/tfq_resolve_parameters_op.cc b/tensorflow_quantum/core/ops/tfq_resolve_parameters_op.cc
index 3ef3277d2..8d7ce06cf 100644
--- a/tensorflow_quantum/core/ops/tfq_resolve_parameters_op.cc
+++ b/tensorflow_quantum/core/ops/tfq_resolve_parameters_op.cc
@@ -59,7 +59,7 @@ class TfqResolveParametersOp : public tensorflow::OpKernel {
             "Number of circuits and values do not match. Got ", programs.size(),
             " circuits and ", maps.size(), " values.")));
 
-    Status parse_status = Status::OK();
+    Status parse_status = ::tensorflow::Status();
     auto p_lock = tensorflow::mutex();
     auto DoWork = [&](int start, int end) {
       std::string temp;
@@ -100,7 +100,7 @@ REGISTER_OP("TfqResolveParameters")
 
       c->set_output(0, c->input(0));
 
-      return tensorflow::Status::OK();
+      return ::tensorflow::Status();
     });
 
 }  // namespace tfq
diff --git a/tensorflow_quantum/core/ops/tfq_simulate_expectation_op.cc b/tensorflow_quantum/core/ops/tfq_simulate_expectation_op.cc
index e8499f482..bca6d2f63 100644
--- a/tensorflow_quantum/core/ops/tfq_simulate_expectation_op.cc
+++ b/tensorflow_quantum/core/ops/tfq_simulate_expectation_op.cc
@@ -86,7 +86,7 @@ class TfqSimulateExpectationOp : public tensorflow::OpKernel {
     std::vector<std::vector<qsim::GateFused<QsimGate>>> fused_circuits(
         programs.size(), std::vector<qsim::GateFused<QsimGate>>({}));
 
-    Status parse_status = Status::OK();
+    Status parse_status = ::tensorflow::Status();
     auto p_lock = tensorflow::mutex();
     auto construct_f = [&](int start, int end) {
       for (int i = start; i < end; i++) {
@@ -186,7 +186,7 @@ class TfqSimulateExpectationOp : public tensorflow::OpKernel {
 
     const int output_dim_op_size = output_tensor->dimension(1);
 
-    Status compute_status = Status::OK();
+    Status compute_status = ::tensorflow::Status();
     auto c_lock = tensorflow::mutex();
     auto DoWork = [&](int start, int end) {
       int old_batch_index = -2;
@@ -274,7 +274,7 @@ REGISTER_OP("TfqSimulateExpectation")
           c->Dim(pauli_sums_shape, 1);
       c->set_output(0, c->Matrix(output_rows, output_cols));
 
-      return tensorflow::Status::OK();
+      return ::tensorflow::Status();
     });
 
 }  // namespace tfq
diff --git a/tensorflow_quantum/core/ops/tfq_simulate_sampled_expectation_op.cc b/tensorflow_quantum/core/ops/tfq_simulate_sampled_expectation_op.cc
index 0452f2750..e0ed05a49 100644
--- a/tensorflow_quantum/core/ops/tfq_simulate_sampled_expectation_op.cc
+++ b/tensorflow_quantum/core/ops/tfq_simulate_sampled_expectation_op.cc
@@ -105,7 +105,7 @@ class TfqSimulateSampledExpectationOp : public tensorflow::OpKernel {
     std::vector<std::vector<qsim::GateFused<QsimGate>>> fused_circuits(
         programs.size(), std::vector<qsim::GateFused<QsimGate>>({}));
 
-    Status parse_status = Status::OK();
+    Status parse_status = ::tensorflow::Status();
     auto p_lock = tensorflow::mutex();
     auto construct_f = [&](int start, int end) {
       for (int i = start; i < end; i++) {
@@ -231,7 +231,7 @@ class TfqSimulateSampledExpectationOp : public tensorflow::OpKernel {
                                 ->tensorflow_cpu_worker_threads()
                                 ->workers->NumThreads();
 
-    Status compute_status = Status::OK();
+    Status compute_status = ::tensorflow::Status();
     auto c_lock = tensorflow::mutex();
     auto DoWork = [&](int start, int end) {
       int old_batch_index = -2;
@@ -333,7 +333,7 @@ REGISTER_OP("TfqSimulateSampledExpectation")
           c->Dim(pauli_sums_shape, 1);
       c->set_output(0, c->Matrix(output_rows, output_cols));
 
-      return tensorflow::Status::OK();
+      return ::tensorflow::Status();
     });
 
 }  // namespace tfq
diff --git a/tensorflow_quantum/core/ops/tfq_simulate_samples_op.cc b/tensorflow_quantum/core/ops/tfq_simulate_samples_op.cc
index f9ea8c4a3..0e68020e9 100644
--- a/tensorflow_quantum/core/ops/tfq_simulate_samples_op.cc
+++ b/tensorflow_quantum/core/ops/tfq_simulate_samples_op.cc
@@ -77,7 +77,7 @@ class TfqSimulateSamplesOp : public tensorflow::OpKernel {
     std::vector<std::vector<qsim::GateFused<QsimGate>>> fused_circuits(
         programs.size(), std::vector<qsim::GateFused<QsimGate>>({}));
 
-    Status parse_status = Status::OK();
+    Status parse_status = ::tensorflow::Status();
     auto p_lock = tensorflow::mutex();
     auto construct_f = [&](int start, int end) {
       for (int i = start; i < end; i++) {
@@ -281,7 +281,7 @@ REGISTER_OP("TfqSimulateSamples")
                   tensorflow::shape_inference::InferenceContext::kUnknownDim,
                   tensorflow::shape_inference::InferenceContext::kUnknownDim}));
 
-      return tensorflow::Status::OK();
+      return ::tensorflow::Status();
     });
 
 }  // namespace tfq
diff --git a/tensorflow_quantum/core/ops/tfq_simulate_state_op.cc b/tensorflow_quantum/core/ops/tfq_simulate_state_op.cc
index 6d74f18b0..e659800ce 100644
--- a/tensorflow_quantum/core/ops/tfq_simulate_state_op.cc
+++ b/tensorflow_quantum/core/ops/tfq_simulate_state_op.cc
@@ -69,7 +69,7 @@ class TfqSimulateStateOp : public tensorflow::OpKernel {
     std::vector<std::vector<qsim::GateFused<QsimGate>>> fused_circuits(
         programs.size(), std::vector<qsim::GateFused<QsimGate>>({}));
 
-    Status parse_status = Status::OK();
+    Status parse_status = ::tensorflow::Status();
     auto p_lock = tensorflow::mutex();
     auto construct_f = [&](int start, int end) {
       for (int i = start; i < end; i++) {
@@ -238,7 +238,7 @@ REGISTER_OP("TfqSimulateState")
                  {c->Dim(programs_shape, 0),
                   tensorflow::shape_inference::InferenceContext::kUnknownDim}));
 
-      return tensorflow::Status::OK();
+      return ::tensorflow::Status();
     });
 
 }  // namespace tfq
diff --git a/tensorflow_quantum/core/ops/tfq_utility_ops_test.py b/tensorflow_quantum/core/ops/tfq_utility_ops_test.py
index 8eab57ae7..00c5ff791 100644
--- a/tensorflow_quantum/core/ops/tfq_utility_ops_test.py
+++ b/tensorflow_quantum/core/ops/tfq_utility_ops_test.py
@@ -82,7 +82,7 @@ def test_append_input_checking(self):
         # These tests really just makes sure we can cast output
         res = tfq_utility_ops.append_circuit([], [])
 
-        self.assertDTypeEqual(res.numpy().astype(np.str), np.dtype('<U1'))
+        self.assertDTypeEqual(res.numpy().astype(str), np.dtype('<U1'))
 
     @parameterized.parameters([{
         'max_n_bits': 20,
diff --git a/tensorflow_quantum/core/serialize/op_serializer.py b/tensorflow_quantum/core/serialize/op_serializer.py
index 1e01b06e6..509216def 100644
--- a/tensorflow_quantum/core/serialize/op_serializer.py
+++ b/tensorflow_quantum/core/serialize/op_serializer.py
@@ -74,7 +74,7 @@ def check_support(func_type: str) -> str:
         msg.arg_value.string_value = value
     elif (isinstance(value, (list, tuple, np.ndarray)) and
           all(isinstance(x, (bool, np.bool_)) for x in value)):
-        # Some protobuf / numpy combinations do not support np.bool_, so cast.
+        # Some protobuf / numpy combinations do not support bool_, so cast.
         msg.arg_value.bool_values.values.extend([bool(x) for x in value])
     elif isinstance(value, sympy.Symbol):
         msg.symbol = str(value.free_symbols.pop())
diff --git a/tensorflow_quantum/core/serialize/op_serializer_test.py b/tensorflow_quantum/core/serialize/op_serializer_test.py
index 7597d6a47..a485091e7 100644
--- a/tensorflow_quantum/core/serialize/op_serializer_test.py
+++ b/tensorflow_quantum/core/serialize/op_serializer_test.py
@@ -108,7 +108,7 @@ def get_val(op):
             }
         }
     }),
-    (List[bool], np.array([True, False], dtype=np.bool), {
+    (List[bool], np.array([True, False], dtype=np.bool_), {
         'arg_value': {
             'bool_values': {
                 'values': [True, False]
diff --git a/tensorflow_quantum/core/src/BUILD b/tensorflow_quantum/core/src/BUILD
index 9e9bf3c17..5595a5ca2 100644
--- a/tensorflow_quantum/core/src/BUILD
+++ b/tensorflow_quantum/core/src/BUILD
@@ -21,6 +21,7 @@ cc_library(
     hdrs = ["adj_util.h"],
     deps = [
         ":circuit_parser_qsim",
+        "@com_google_absl//absl/status",
         "@qsim//lib:circuit",
         "@qsim//lib:fuser",
         "@qsim//lib:fuser_basic",
@@ -38,6 +39,8 @@ cc_test(
         ":adj_util",
         ":circuit_parser_qsim",
         "@com_google_googletest//:gtest_main",
+        "@com_google_absl//absl/status",
+        "@local_config_tf//:libtensorflow_framework",
         "@qsim//lib:gates_cirq",
         "@qsim//lib:matrix",
     ],
@@ -52,6 +55,7 @@ cc_library(
         "//tensorflow_quantum/core/proto:program_cc_proto",
         "//tensorflow_quantum/core/proto:projector_sum_cc_proto",
         "@com_google_absl//absl/container:flat_hash_map",
+        "@com_google_absl//absl/status",
         "@local_config_tf//:libtensorflow_framework",
         "@local_config_tf//:tf_header_lib",
         "@qsim//lib:channel",
@@ -127,6 +131,7 @@ cc_library(
         "@com_google_absl//absl/container:flat_hash_map",
         "@com_google_absl//absl/container:flat_hash_set",
         "@com_google_absl//absl/strings",
+        "@com_google_absl//absl/status",
         "@local_config_tf//:libtensorflow_framework",
         "@local_config_tf//:tf_header_lib",
     ],
@@ -141,7 +146,9 @@ cc_test(
         ":program_resolution",
         "//tensorflow_quantum/core/proto:program_cc_proto",
         "@com_google_absl//absl/container:flat_hash_map",
+        "@com_google_absl//absl/status",
         "@com_google_googletest//:gtest_main",
+        "@local_config_tf//:libtensorflow_framework",
         "@local_config_tf//:tf_header_lib",
     ],
 )
diff --git a/tensorflow_quantum/core/src/circuit_parser_qsim.cc b/tensorflow_quantum/core/src/circuit_parser_qsim.cc
index 8a7b2d490..f641c64cd 100644
--- a/tensorflow_quantum/core/src/circuit_parser_qsim.cc
+++ b/tensorflow_quantum/core/src/circuit_parser_qsim.cc
@@ -29,6 +29,7 @@ limitations under the License.
 #include "absl/container/flat_hash_map.h"
 #include "absl/strings/numbers.h"
 #include "absl/strings/str_split.h"
+#include "absl/strings/string_view.h"
 #include "absl/types/optional.h"
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow_quantum/core/proto/pauli_sum.pb.h"
@@ -57,7 +58,8 @@ inline Status ParseProtoArg(
   // iterator<Map<str, Arg>>
   const auto arg_v = op.args().find(arg_name);
   if (arg_v == op.args().end()) {
-    return Status(tensorflow::error::INVALID_ARGUMENT,
+    return Status(static_cast<tensorflow::errors::Code>(
+            absl::StatusCode::kInvalidArgument),
                   "Could not find arg: " + arg_name + " in op.");
   }
   // find proto arg field.
@@ -69,7 +71,8 @@ inline Status ParseProtoArg(
     const auto iter = param_map.find(proto_arg.symbol());
     if (iter == param_map.end()) {
       return Status(
-          tensorflow::error::INVALID_ARGUMENT,
+          static_cast<tensorflow::errors::Code>(
+            absl::StatusCode::kInvalidArgument),
           "Could not find symbol in parameter map: " + proto_arg.symbol());
     }
     *result = iter->second.second;
@@ -77,7 +80,7 @@ inline Status ParseProtoArg(
       symbol_used->emplace(iter->first);
     }
   }
-  return Status::OK();
+  return ::tensorflow::Status();
 }
 
 inline Status ParseProtoControls(const Operation& op,
@@ -91,7 +94,7 @@ inline Status ParseProtoControls(const Operation& op,
 
   if (control_str == "" && control_v_str == "") {
     // empty default value set in serializer.py
-    return Status::OK();
+    return ::tensorflow::Status();
   }
 
   std::vector<absl::string_view> control_toks =
@@ -100,11 +103,12 @@ inline Status ParseProtoControls(const Operation& op,
       absl::StrSplit(control_v_str, ',');
 
   if (control_toks.size() != control_v_toks.size()) {
-    return Status(tensorflow::error::INVALID_ARGUMENT,
+    return Status(static_cast<tensorflow::errors::Code>(
+            absl::StatusCode::kInvalidArgument),
                   "Mistmatched number of control qubits and control values.");
   }
   if (control_toks.empty()) {
-    return Status::OK();
+    return ::tensorflow::Status();
   }
   bool valid;
   unsigned int tmp;
@@ -119,12 +123,13 @@ inline Status ParseProtoControls(const Operation& op,
   for (auto tok : control_v_toks) {
     valid = absl::SimpleAtoi(tok, &tmp);
     if (!valid) {
-      return Status(tensorflow::error::INVALID_ARGUMENT,
+      return Status(static_cast<tensorflow::errors::Code>(
+            absl::StatusCode::kInvalidArgument),
                     "Unparseable control value: " + std::string(tok));
     }
     control_values->push_back(tmp);
   }
-  return Status::OK();
+  return ::tensorflow::Status();
 }
 
 inline Status OptionalInsertControls(const Operation& op,
@@ -138,10 +143,10 @@ inline Status OptionalInsertControls(const Operation& op,
     return s;
   }
   if (control_qubits.empty()) {
-    return Status::OK();
+    return ::tensorflow::Status();
   }
   qsim::MakeControlledGate(control_qubits, control_values, *gate);
-  return Status::OK();
+  return ::tensorflow::Status();
 }
 
 // series of fixed signature gate builders.
@@ -170,7 +175,7 @@ inline Status SingleConstantGate(
     info.index = circuit->gates.size() - 1;
     metadata->push_back(info);
   }
-  return Status::OK();
+  return ::tensorflow::Status();
 }
 
 // two qubit gate Create(time, q0, q1)
@@ -196,7 +201,7 @@ inline Status TwoConstantGate(
     info.index = circuit->gates.size() - 1;
     metadata->push_back(info);
   }
-  return Status::OK();
+  return ::tensorflow::Status();
 }
 
 // single qubit eigen -> Create(time, q0, exponent, global_shift)
@@ -245,7 +250,7 @@ inline Status SingleEigenGate(
     }
     metadata->push_back(info);
   }
-  return Status::OK();
+  return ::tensorflow::Status();
 }
 
 // two qubit eigen -> Create(time, q0, q1, exp, gs)
@@ -296,7 +301,7 @@ inline Status TwoEigenGate(
     }
     metadata->push_back(info);
   }
-  return Status::OK();
+  return ::tensorflow::Status();
 }
 
 Status IGate(const Operation& op, const SymbolMap& param_map,
@@ -447,7 +452,7 @@ inline Status PhasedXGate(const Operation& op, const SymbolMap& param_map,
     }
     metadata->push_back(info);
   }
-  return Status::OK();
+  return ::tensorflow::Status();
 }
 
 // two qubit fsim -> Create(time, q0, q1, theta, phi)
@@ -504,7 +509,7 @@ inline Status FsimGate(const Operation& op, const SymbolMap& param_map,
     }
     metadata->push_back(info);
   }
-  return Status::OK();
+  return ::tensorflow::Status();
 }
 
 // two qubit phase iswap -> Create(time, q0, q1, pexp, exp)
@@ -562,7 +567,7 @@ inline Status PhasedISwapGate(const Operation& op, const SymbolMap& param_map,
     }
     metadata->push_back(info);
   }
-  return Status::OK();
+  return ::tensorflow::Status();
 }
 
 tensorflow::Status ParseAppendGate(const Operation& op,
@@ -590,7 +595,8 @@ tensorflow::Status ParseAppendGate(const Operation& op,
   auto build_f = func_map.find(op.gate().id());
   if (build_f == func_map.end()) {
     *lookup_succeeded = false;
-    return Status(tensorflow::error::INVALID_ARGUMENT,
+    return Status(static_cast<tensorflow::errors::Code>(
+            absl::StatusCode::kInvalidArgument),
                   absl::StrCat("Could not parse gate id: ", op.gate().id(),
                                ". This is likely because a cirq.Channel was "
                                "used in an op that does not support them."));
@@ -618,7 +624,7 @@ inline Status AsymmetricDepolarizingChannel(const Operation& op,
   auto chan = qsim::Cirq::AsymmetricDepolarizingChannel<float>::Create(
       time, num_qubits - q - 1, p_x, p_y, p_z);
   ncircuit->channels.push_back(chan);
-  return Status::OK();
+  return ::tensorflow::Status();
 }
 
 inline Status DepolarizingChannel(const Operation& op,
@@ -638,7 +644,7 @@ inline Status DepolarizingChannel(const Operation& op,
   auto chan = qsim::Cirq::DepolarizingChannel<float>::Create(
       time, num_qubits - q - 1, p);
   ncircuit->channels.push_back(chan);
-  return Status::OK();
+  return ::tensorflow::Status();
 }
 
 inline Status GADChannel(const Operation& op, const unsigned int num_qubits,
@@ -661,7 +667,7 @@ inline Status GADChannel(const Operation& op, const unsigned int num_qubits,
   auto chan = qsim::Cirq::GeneralizedAmplitudeDampingChannel<float>::Create(
       time, num_qubits - q - 1, p, gamma);
   ncircuit->channels.push_back(chan);
-  return Status::OK();
+  return ::tensorflow::Status();
 }
 
 inline Status ResetChannel(const Operation& op, const unsigned int num_qubits,
@@ -673,7 +679,7 @@ inline Status ResetChannel(const Operation& op, const unsigned int num_qubits,
 
   auto chan = qsim::Cirq::ResetChannel<float>::Create(time, num_qubits - q - 1);
   ncircuit->channels.push_back(chan);
-  return Status::OK();
+  return ::tensorflow::Status();
 }
 
 inline Status AmplitudeDampingChannel(const Operation& op,
@@ -693,7 +699,7 @@ inline Status AmplitudeDampingChannel(const Operation& op,
   auto chan = qsim::Cirq::AmplitudeDampingChannel<float>::Create(
       time, num_qubits - q - 1, gamma);
   ncircuit->channels.push_back(chan);
-  return Status::OK();
+  return ::tensorflow::Status();
 }
 
 inline Status PhaseDampingChannel(const Operation& op,
@@ -714,7 +720,7 @@ inline Status PhaseDampingChannel(const Operation& op,
   auto chan = qsim::Cirq::PhaseDampingChannel<float>::Create(
       time, num_qubits - q - 1, gamma);
   ncircuit->channels.push_back(chan);
-  return Status::OK();
+  return ::tensorflow::Status();
 }
 
 inline Status PhaseFlipChannel(const Operation& op,
@@ -735,7 +741,7 @@ inline Status PhaseFlipChannel(const Operation& op,
   auto chan =
       qsim::Cirq::PhaseFlipChannel<float>::Create(time, num_qubits - q - 1, p);
   ncircuit->channels.push_back(chan);
-  return Status::OK();
+  return ::tensorflow::Status();
 }
 
 inline Status BitFlipChannel(const Operation& op, const unsigned int num_qubits,
@@ -755,7 +761,7 @@ inline Status BitFlipChannel(const Operation& op, const unsigned int num_qubits,
   auto chan =
       qsim::Cirq::BitFlipChannel<float>::Create(time, num_qubits - q - 1, p);
   ncircuit->channels.push_back(chan);
-  return Status::OK();
+  return ::tensorflow::Status();
 }
 
 tensorflow::Status ParseAppendChannel(const Operation& op,
@@ -774,7 +780,8 @@ tensorflow::Status ParseAppendChannel(const Operation& op,
 
   auto build_f = chan_func_map.find(op.gate().id());
   if (build_f == chan_func_map.end()) {
-    return Status(tensorflow::error::INVALID_ARGUMENT,
+    return Status(static_cast<tensorflow::errors::Code>(
+            absl::StatusCode::kInvalidArgument),
                   absl::StrCat("Could not parse channel id: ", op.gate().id()));
   }
   return build_f->second(op, num_qubits, time, ncircuit);
@@ -790,7 +797,7 @@ tensorflow::Status NoisyQsimCircuitFromProgram(const Program& program,
   // Special case empty.
   ncircuit->num_qubits = num_qubits;
   if (num_qubits <= 0) {
-    return Status::OK();
+    return ::tensorflow::Status();
   }
 
   int time = 0;
@@ -834,7 +841,7 @@ tensorflow::Status NoisyQsimCircuitFromProgram(const Program& program,
           {qsim::gate::Measurement<QsimGate>::Create(time, all_qbs)}}});
   }
 
-  return Status::OK();
+  return ::tensorflow::Status();
 }
 
 tensorflow::Status QsimCircuitFromProgram(
@@ -847,7 +854,7 @@ tensorflow::Status QsimCircuitFromProgram(
   bool unused;
   // Special case empty.
   if (num_qubits <= 0) {
-    return Status::OK();
+    return ::tensorflow::Status();
   }
 
   circuit->gates.reserve(program.circuit().moments_size() * num_qubits);
@@ -869,7 +876,7 @@ tensorflow::Status QsimCircuitFromProgram(
   *fused_circuit = qsim::BasicGateFuser<qsim::IO, QsimGate>().FuseGates(
       qsim::BasicGateFuser<qsim::IO, QsimGate>::Parameter(),
       circuit->num_qubits, circuit->gates);
-  return Status::OK();
+  return ::tensorflow::Status();
 }
 
 Status QsimCircuitFromPauliTerm(
diff --git a/tensorflow_quantum/core/src/circuit_parser_qsim_test.cc b/tensorflow_quantum/core/src/circuit_parser_qsim_test.cc
index 4cfb40424..dea73d719 100644
--- a/tensorflow_quantum/core/src/circuit_parser_qsim_test.cc
+++ b/tensorflow_quantum/core/src/circuit_parser_qsim_test.cc
@@ -149,7 +149,7 @@ TEST_P(TwoQubitEigenFixture, TwoEigenGate) {
   // Test case where we have a placeholder.
   ASSERT_EQ(QsimCircuitFromProgram(program_proto, symbol_map, 2, &test_circuit,
                                    &fused_circuit, &metadata),
-            tensorflow::Status::OK());
+            ::tensorflow::Status());
   AssertTwoQubitEqual(test_circuit.gates[0], ref_gate);
   EXPECT_EQ(metadata[0].index, 0);
   EXPECT_EQ(metadata[0].symbol_values[0], "placeholder");
@@ -172,7 +172,7 @@ TEST_P(TwoQubitEigenFixture, TwoEigenGate) {
   // Test case where we have all float values.
   ASSERT_EQ(QsimCircuitFromProgram(program_proto, symbol_map, 2, &test_circuit,
                                    &fused_circuit, &metadata),
-            tensorflow::Status::OK());
+            ::tensorflow::Status());
   AssertTwoQubitEqual(test_circuit.gates[0], ref_gate);
   EXPECT_EQ(metadata[0].index, 0);
   EXPECT_NEAR(metadata[0].gate_params[0], exp, 1e-5);
@@ -191,7 +191,8 @@ TEST_P(TwoQubitEigenFixture, TwoEigenGate) {
   // Test case where proto arg missing.
   ASSERT_EQ(QsimCircuitFromProgram(program_proto, symbol_map, 2, &test_circuit,
                                    &fused_circuit),
-            tensorflow::Status(tensorflow::error::INVALID_ARGUMENT,
+            tensorflow::Status(static_cast<tensorflow::error::Code>(
+            absl::StatusCode::kInvalidArgument),
                                "Could not find arg: exponent in op."));
 
   test_circuit.gates.clear();
@@ -203,7 +204,8 @@ TEST_P(TwoQubitEigenFixture, TwoEigenGate) {
   ASSERT_EQ(
       QsimCircuitFromProgram(program_proto, symbol_map, 2, &test_circuit,
                              &fused_circuit),
-      tensorflow::Status(tensorflow::error::INVALID_ARGUMENT,
+      tensorflow::Status(static_cast<tensorflow::error::Code>(
+            absl::StatusCode::kInvalidArgument),
                          "Could not find symbol in parameter map: alpha"));
 }
 
@@ -250,7 +252,7 @@ TEST_P(TwoQubitEigenFixture, TwoEigenGateControlled) {
   // Test case where we have a placeholder.
   ASSERT_EQ(QsimCircuitFromProgram(program_proto, symbol_map, 4, &test_circuit,
                                    &fused_circuit, &metadata),
-            tensorflow::Status::OK());
+            ::tensorflow::Status());
   AssertTwoQubitEqual(test_circuit.gates[0], ref_gate);
   EXPECT_EQ(metadata[0].index, 0);
   EXPECT_EQ(metadata[0].symbol_values[0], "placeholder");
@@ -319,7 +321,7 @@ TEST_P(SingleQubitEigenFixture, SingleEigenGate) {
 
   ASSERT_EQ(QsimCircuitFromProgram(program_proto, symbol_map, 1, &test_circuit,
                                    &fused_circuit, &metadata),
-            tensorflow::Status::OK());
+            ::tensorflow::Status());
   AssertOneQubitEqual(test_circuit.gates[0], ref_gate);
   EXPECT_EQ(metadata[0].index, 0);
   EXPECT_EQ(metadata[0].symbol_values[0], "placeholder");
@@ -342,7 +344,7 @@ TEST_P(SingleQubitEigenFixture, SingleEigenGate) {
   // Test case where we have all float values.
   ASSERT_EQ(QsimCircuitFromProgram(program_proto, symbol_map, 1, &test_circuit,
                                    &fused_circuit, &metadata),
-            tensorflow::Status::OK());
+            ::tensorflow::Status());
   AssertOneQubitEqual(test_circuit.gates[0], ref_gate);
   EXPECT_EQ(metadata[0].index, 0);
   EXPECT_NEAR(metadata[0].gate_params[0], exp, 1e-5);
@@ -361,7 +363,8 @@ TEST_P(SingleQubitEigenFixture, SingleEigenGate) {
   // Test case where proto arg missing.
   ASSERT_EQ(QsimCircuitFromProgram(program_proto, symbol_map, 1, &test_circuit,
                                    &fused_circuit),
-            tensorflow::Status(tensorflow::error::INVALID_ARGUMENT,
+            tensorflow::Status(static_cast<tensorflow::error::Code>(
+            absl::StatusCode::kInvalidArgument),
                                "Could not find arg: exponent in op."));
 
   test_circuit.gates.clear();
@@ -373,7 +376,8 @@ TEST_P(SingleQubitEigenFixture, SingleEigenGate) {
   ASSERT_EQ(
       QsimCircuitFromProgram(program_proto, symbol_map, 1, &test_circuit,
                              &fused_circuit),
-      tensorflow::Status(tensorflow::error::INVALID_ARGUMENT,
+      tensorflow::Status(static_cast<tensorflow::error::Code>(
+            absl::StatusCode::kInvalidArgument),
                          "Could not find symbol in parameter map: alpha"));
 }
 
@@ -418,7 +422,7 @@ TEST_P(SingleQubitEigenFixture, SingleEigenGateControlled) {
 
   ASSERT_EQ(QsimCircuitFromProgram(program_proto, symbol_map, 3, &test_circuit,
                                    &fused_circuit, &metadata),
-            tensorflow::Status::OK());
+            ::tensorflow::Status());
   AssertOneQubitEqual(test_circuit.gates[0], ref_gate);
   EXPECT_EQ(metadata[0].index, 0);
   EXPECT_EQ(metadata[0].symbol_values[0], "placeholder");
@@ -471,7 +475,7 @@ TEST(QsimCircuitParserTest, SingleConstantGate) {
 
     ASSERT_EQ(QsimCircuitFromProgram(program_proto, empty_map, 1, &test_circuit,
                                      &fused_circuit, &metadata),
-              tensorflow::Status::OK());
+              ::tensorflow::Status());
     AssertOneQubitEqual(test_circuit.gates[0], kv.second);
     EXPECT_EQ(metadata.size(), 1);
     EXPECT_EQ(metadata[0].placeholder_names.size(), 0);
@@ -511,7 +515,7 @@ TEST(QsimCircuitParserTest, SingleConstantGateControlled) {
 
     ASSERT_EQ(QsimCircuitFromProgram(program_proto, empty_map, 3, &test_circuit,
                                      &fused_circuit, &metadata),
-              tensorflow::Status::OK());
+              ::tensorflow::Status());
     AssertOneQubitEqual(test_circuit.gates[0], kv.second);
     EXPECT_EQ(metadata.size(), 1);
     EXPECT_EQ(metadata[0].placeholder_names.size(), 0);
@@ -553,7 +557,7 @@ TEST(QsimCircuitParserTest, TwoConstantGate) {
 
     ASSERT_EQ(QsimCircuitFromProgram(program_proto, empty_map, 2, &test_circuit,
                                      &fused_circuit, &metadata),
-              tensorflow::Status::OK());
+              ::tensorflow::Status());
     AssertTwoQubitEqual(test_circuit.gates[0], kv.second);
     EXPECT_EQ(metadata.size(), 1);
     EXPECT_EQ(metadata[0].placeholder_names.size(), 0);
@@ -596,7 +600,7 @@ TEST(QsimCircuitParserTest, TwoConstantGateControlled) {
 
     ASSERT_EQ(QsimCircuitFromProgram(program_proto, empty_map, 4, &test_circuit,
                                      &fused_circuit, &metadata),
-              tensorflow::Status::OK());
+              ::tensorflow::Status());
     AssertTwoQubitEqual(test_circuit.gates[0], kv.second);
     EXPECT_EQ(metadata.size(), 1);
     EXPECT_EQ(metadata[0].placeholder_names.size(), 0);
@@ -646,7 +650,7 @@ TEST(QsimCircuitParserTest, FsimGate) {
   // Test symbol resolution.
   ASSERT_EQ(QsimCircuitFromProgram(program_proto, symbol_map, 2, &test_circuit,
                                    &fused_circuit, &metadata),
-            tensorflow::Status::OK());
+            ::tensorflow::Status());
   AssertTwoQubitEqual(test_circuit.gates[0], reference);
   EXPECT_EQ(metadata.size(), 1);
   EXPECT_EQ(metadata[0].placeholder_names.size(), 2);
@@ -673,7 +677,7 @@ TEST(QsimCircuitParserTest, FsimGate) {
   // Test float values only.
   ASSERT_EQ(QsimCircuitFromProgram(program_proto, symbol_map, 2, &test_circuit,
                                    &fused_circuit, &metadata),
-            tensorflow::Status::OK());
+            ::tensorflow::Status());
   AssertTwoQubitEqual(test_circuit.gates[0], reference);
   EXPECT_EQ(metadata.size(), 1);
   EXPECT_EQ(metadata[0].placeholder_names.size(), 0);
@@ -691,7 +695,8 @@ TEST(QsimCircuitParserTest, FsimGate) {
   // Test case where proto arg missing.
   ASSERT_EQ(QsimCircuitFromProgram(program_proto, symbol_map, 2, &test_circuit,
                                    &fused_circuit),
-            tensorflow::Status(tensorflow::error::INVALID_ARGUMENT,
+            tensorflow::Status(static_cast<tensorflow::error::Code>(
+            absl::StatusCode::kInvalidArgument),
                                "Could not find arg: theta in op."));
 
   test_circuit.gates.clear();
@@ -703,7 +708,8 @@ TEST(QsimCircuitParserTest, FsimGate) {
   ASSERT_EQ(
       QsimCircuitFromProgram(program_proto, symbol_map, 2, &test_circuit,
                              &fused_circuit),
-      tensorflow::Status(tensorflow::error::INVALID_ARGUMENT,
+      tensorflow::Status(static_cast<tensorflow::error::Code>(
+            absl::StatusCode::kInvalidArgument),
                          "Could not find symbol in parameter map: alpha"));
 }
 
@@ -749,7 +755,7 @@ TEST(QsimCircuitParserTest, FsimGateControlled) {
   // Test symbol resolution.
   ASSERT_EQ(QsimCircuitFromProgram(program_proto, symbol_map, 4, &test_circuit,
                                    &fused_circuit, &metadata),
-            tensorflow::Status::OK());
+            ::tensorflow::Status());
   AssertTwoQubitEqual(test_circuit.gates[0], reference);
   EXPECT_EQ(metadata.size(), 1);
   EXPECT_EQ(metadata[0].placeholder_names.size(), 2);
@@ -808,7 +814,7 @@ TEST(QsimCircuitParserTest, PhasedISwap) {
   // Test symbol resolution.
   ASSERT_EQ(QsimCircuitFromProgram(program_proto, symbol_map, 2, &test_circuit,
                                    &fused_circuit, &metadata),
-            tensorflow::Status::OK());
+            ::tensorflow::Status());
   AssertTwoQubitEqual(test_circuit.gates[0], reference);
   EXPECT_EQ(metadata.size(), 1);
   EXPECT_EQ(metadata[0].placeholder_names.size(), 2);
@@ -835,7 +841,7 @@ TEST(QsimCircuitParserTest, PhasedISwap) {
   // Test float values only.
   ASSERT_EQ(QsimCircuitFromProgram(program_proto, symbol_map, 2, &test_circuit,
                                    &fused_circuit, &metadata),
-            tensorflow::Status::OK());
+            ::tensorflow::Status());
   AssertTwoQubitEqual(test_circuit.gates[0], reference);
   EXPECT_EQ(metadata.size(), 1);
   EXPECT_EQ(metadata[0].placeholder_names.size(), 0);
@@ -853,7 +859,8 @@ TEST(QsimCircuitParserTest, PhasedISwap) {
   // Test case where proto arg missing.
   ASSERT_EQ(QsimCircuitFromProgram(program_proto, symbol_map, 2, &test_circuit,
                                    &fused_circuit),
-            tensorflow::Status(tensorflow::error::INVALID_ARGUMENT,
+            tensorflow::Status(static_cast<tensorflow::error::Code>(
+            absl::StatusCode::kInvalidArgument),
                                "Could not find arg: phase_exponent in op."));
 
   test_circuit.gates.clear();
@@ -865,7 +872,8 @@ TEST(QsimCircuitParserTest, PhasedISwap) {
   ASSERT_EQ(
       QsimCircuitFromProgram(program_proto, symbol_map, 2, &test_circuit,
                              &fused_circuit),
-      tensorflow::Status(tensorflow::error::INVALID_ARGUMENT,
+      tensorflow::Status(static_cast<tensorflow::error::Code>(
+            absl::StatusCode::kInvalidArgument),
                          "Could not find symbol in parameter map: alpha"));
 }
 
@@ -913,7 +921,7 @@ TEST(QsimCircuitParserTest, PhasedISwapControlled) {
   // Test symbol resolution.
   ASSERT_EQ(QsimCircuitFromProgram(program_proto, symbol_map, 4, &test_circuit,
                                    &fused_circuit, &metadata),
-            tensorflow::Status::OK());
+            ::tensorflow::Status());
   AssertTwoQubitEqual(test_circuit.gates[0], reference);
   EXPECT_EQ(metadata.size(), 1);
   EXPECT_EQ(metadata[0].placeholder_names.size(), 2);
@@ -972,7 +980,7 @@ TEST(QsimCircuitParserTest, PhasedXPow) {
   // Test symbol resolution.
   ASSERT_EQ(QsimCircuitFromProgram(program_proto, symbol_map, 1, &test_circuit,
                                    &fused_circuit, &metadata),
-            tensorflow::Status::OK());
+            ::tensorflow::Status());
   AssertOneQubitEqual(test_circuit.gates[0], reference);
   EXPECT_EQ(metadata.size(), 1);
   EXPECT_EQ(metadata[0].placeholder_names.size(), 2);
@@ -1001,7 +1009,7 @@ TEST(QsimCircuitParserTest, PhasedXPow) {
   // Test float values only.
   ASSERT_EQ(QsimCircuitFromProgram(program_proto, symbol_map, 1, &test_circuit,
                                    &fused_circuit, &metadata),
-            tensorflow::Status::OK());
+            ::tensorflow::Status());
   AssertOneQubitEqual(test_circuit.gates[0], reference);
   EXPECT_EQ(metadata.size(), 1);
   EXPECT_EQ(metadata[0].placeholder_names.size(), 0);
@@ -1019,7 +1027,8 @@ TEST(QsimCircuitParserTest, PhasedXPow) {
   // Test case where proto arg missing.
   ASSERT_EQ(QsimCircuitFromProgram(program_proto, symbol_map, 1, &test_circuit,
                                    &fused_circuit),
-            tensorflow::Status(tensorflow::error::INVALID_ARGUMENT,
+            tensorflow::Status(static_cast<tensorflow::error::Code>(
+            absl::StatusCode::kInvalidArgument),
                                "Could not find arg: phase_exponent in op."));
 
   test_circuit.gates.clear();
@@ -1031,7 +1040,8 @@ TEST(QsimCircuitParserTest, PhasedXPow) {
   ASSERT_EQ(
       QsimCircuitFromProgram(program_proto, symbol_map, 1, &test_circuit,
                              &fused_circuit),
-      tensorflow::Status(tensorflow::error::INVALID_ARGUMENT,
+      tensorflow::Status(static_cast<tensorflow::error::Code>(
+            absl::StatusCode::kInvalidArgument),
                          "Could not find symbol in parameter map: alpha"));
 }
 
@@ -1079,7 +1089,7 @@ TEST(QsimCircuitParserTest, PhasedXPowControlled) {
   // Test symbol resolution.
   ASSERT_EQ(QsimCircuitFromProgram(program_proto, symbol_map, 3, &test_circuit,
                                    &fused_circuit, &metadata),
-            tensorflow::Status::OK());
+            ::tensorflow::Status());
   AssertOneQubitEqual(test_circuit.gates[0], reference);
   EXPECT_EQ(metadata.size(), 1);
   EXPECT_EQ(metadata[0].placeholder_names.size(), 2);
@@ -1124,7 +1134,8 @@ TEST(QsimCircuitParserTest, InvalidControlValues) {
 
   ASSERT_EQ(QsimCircuitFromProgram(program_proto, empty_map, 3, &test_circuit,
                                    &fused_circuit, &metadata),
-            tensorflow::Status(tensorflow::error::INVALID_ARGUMENT,
+            tensorflow::Status(static_cast<tensorflow::error::Code>(
+            absl::StatusCode::kInvalidArgument),
                                "Unparseable control value: junk"));
 }
 
@@ -1157,7 +1168,8 @@ TEST(QsimCircuitParserTest, MismatchControlNum) {
   ASSERT_EQ(QsimCircuitFromProgram(program_proto, empty_map, 3, &test_circuit,
                                    &fused_circuit, &metadata),
             tensorflow::Status(
-                tensorflow::error::INVALID_ARGUMENT,
+                static_cast<tensorflow::error::Code>(
+            absl::StatusCode::kInvalidArgument),
                 "Mistmatched number of control qubits and control values."));
 }
 
@@ -1174,7 +1186,7 @@ TEST(QsimCircuitParserTest, EmptyTest) {
   // Ensure that nothing bad happens with an empty circuit.
   ASSERT_EQ(QsimCircuitFromProgram(program_proto, empty_map, 2, &test_circuit,
                                    &fused_circuit, &metadata),
-            tensorflow::Status::OK());
+            ::tensorflow::Status());
   ASSERT_EQ(test_circuit.gates.size(), 0);
   ASSERT_EQ(fused_circuit.size(), 0);
   ASSERT_EQ(metadata.size(), 0);
@@ -1227,7 +1239,7 @@ TEST(QsimCircuitParserTest, CompoundCircuit) {
 
   ASSERT_EQ(
       NoisyQsimCircuitFromProgram(program_proto, {}, 2, true, &test_circuit),
-      tensorflow::Status::OK());
+      ::tensorflow::Status());
   AssertChannelEqual(test_circuit.channels[0], ref_chan);
   AssertOneQubitEqual(test_circuit.channels[1][0].ops[0], ref_gate);
   ASSERT_EQ(test_circuit.channels.size(),
@@ -1270,7 +1282,7 @@ TEST(QsimCircuitParserTest, AsymmetricDepolarizing) {
 
   ASSERT_EQ(
       NoisyQsimCircuitFromProgram(program_proto, {}, 1, false, &test_circuit),
-      tensorflow::Status::OK());
+      ::tensorflow::Status());
   AssertChannelEqual(test_circuit.channels[0], reference);
   ASSERT_EQ(test_circuit.channels.size(), 1);
   ASSERT_EQ(test_circuit.num_qubits, 1);
@@ -1307,7 +1319,7 @@ TEST(QsimCircuitParserTest, AmplitudeDamping) {
 
   ASSERT_EQ(
       NoisyQsimCircuitFromProgram(program_proto, {}, 1, false, &test_circuit),
-      tensorflow::Status::OK());
+      ::tensorflow::Status());
   AssertChannelEqual(test_circuit.channels[0], reference);
   ASSERT_EQ(test_circuit.channels.size(), 1);
   ASSERT_EQ(test_circuit.num_qubits, 1);
@@ -1343,7 +1355,7 @@ TEST(QsimCircuitParserTest, Depolarizing) {
 
   ASSERT_EQ(
       NoisyQsimCircuitFromProgram(program_proto, {}, 1, false, &test_circuit),
-      tensorflow::Status::OK());
+      ::tensorflow::Status());
   AssertChannelEqual(test_circuit.channels[0], reference);
   ASSERT_EQ(test_circuit.channels.size(), 1);
   ASSERT_EQ(test_circuit.num_qubits, 1);
@@ -1383,7 +1395,7 @@ TEST(QsimCircuitParserTest, GeneralizedAmplitudeDamping) {
 
   ASSERT_EQ(
       NoisyQsimCircuitFromProgram(program_proto, {}, 1, false, &test_circuit),
-      tensorflow::Status::OK());
+      ::tensorflow::Status());
   AssertChannelEqual(test_circuit.channels[0], reference);
   ASSERT_EQ(test_circuit.channels.size(), 1);
   ASSERT_EQ(test_circuit.num_qubits, 1);
@@ -1417,7 +1429,7 @@ TEST(QsimCircuitParserTest, Reset) {
 
   ASSERT_EQ(
       NoisyQsimCircuitFromProgram(program_proto, {}, 1, false, &test_circuit),
-      tensorflow::Status::OK());
+      ::tensorflow::Status());
   AssertChannelEqual(test_circuit.channels[0], reference);
   ASSERT_EQ(test_circuit.channels.size(), 1);
   ASSERT_EQ(test_circuit.num_qubits, 1);
@@ -1453,7 +1465,7 @@ TEST(QsimCircuitParserTest, PhaseDamping) {
 
   ASSERT_EQ(
       NoisyQsimCircuitFromProgram(program_proto, {}, 1, false, &test_circuit),
-      tensorflow::Status::OK());
+      ::tensorflow::Status());
   AssertChannelEqual(test_circuit.channels[0], reference);
   ASSERT_EQ(test_circuit.channels.size(), 1);
   ASSERT_EQ(test_circuit.num_qubits, 1);
@@ -1489,7 +1501,7 @@ TEST(QsimCircuitParserTest, PhaseFlip) {
 
   ASSERT_EQ(
       NoisyQsimCircuitFromProgram(program_proto, {}, 1, false, &test_circuit),
-      tensorflow::Status::OK());
+      ::tensorflow::Status());
   AssertChannelEqual(test_circuit.channels[0], reference);
   ASSERT_EQ(test_circuit.channels.size(), 1);
   ASSERT_EQ(test_circuit.num_qubits, 1);
@@ -1525,7 +1537,7 @@ TEST(QsimCircuitParserTest, BitFlip) {
 
   ASSERT_EQ(
       NoisyQsimCircuitFromProgram(program_proto, {}, 1, false, &test_circuit),
-      tensorflow::Status::OK());
+      ::tensorflow::Status());
   AssertChannelEqual(test_circuit.channels[0], reference);
   ASSERT_EQ(test_circuit.channels.size(), 1);
   ASSERT_EQ(test_circuit.num_qubits, 1);
@@ -1540,7 +1552,7 @@ TEST(QsimCircuitParserTest, NoisyEmpty) {
   NoisyQsimCircuit test_circuit;
   ASSERT_EQ(
       NoisyQsimCircuitFromProgram(program_proto, {}, 0, false, &test_circuit),
-      tensorflow::Status::OK());
+      ::tensorflow::Status());
   ASSERT_EQ(test_circuit.channels.size(), 0);
   ASSERT_EQ(test_circuit.num_qubits, 0);
 }
@@ -1559,7 +1571,8 @@ TEST(QsimCircuitParserTest, NoisyBadProto) {
   NoisyQsimCircuit test_circuit;
   ASSERT_EQ(
       NoisyQsimCircuitFromProgram(program_proto, {}, 1, false, &test_circuit),
-      tensorflow::Status(tensorflow::error::INVALID_ARGUMENT,
+      tensorflow::Status(static_cast<tensorflow::error::Code>(
+            absl::StatusCode::kInvalidArgument),
                          "Could not parse channel id: ABCDEFG"));
 }
 
@@ -1580,7 +1593,7 @@ TEST(QsimCircuitParserTest, CircuitFromPauliTermPauli) {
   // Check conversion
   status =
       QsimCircuitFromPauliTerm(pauli_proto, 1, &test_circuit, &fused_circuit);
-  ASSERT_EQ(status, tensorflow::Status::OK());
+  ASSERT_EQ(status, tensorflow::Status());
   ASSERT_EQ(test_circuit.num_qubits, 1);
   ASSERT_EQ(test_circuit.gates.size(), 1);
   AssertOneQubitEqual(test_circuit.gates[0], reference);
@@ -1593,7 +1606,7 @@ TEST(QsimCircuitParserTest, CircuitFromPauliTermEmpty) {
   std::vector<qsim::GateFused<QsimGate>> fused_circuit;
   status =
       QsimCircuitFromPauliTerm(pauli_proto, 0, &test_circuit, &fused_circuit);
-  ASSERT_EQ(status, tensorflow::Status::OK());
+  ASSERT_EQ(status, tensorflow::Status());
   ASSERT_EQ(test_circuit.num_qubits, 0);
   ASSERT_EQ(test_circuit.gates.size(), 0);
 }
@@ -1615,7 +1628,7 @@ TEST(QsimCircuitParserTest, ZBasisCircuitFromPauliTermPauliX) {
   // Check conversion
   status = QsimZBasisCircuitFromPauliTerm(pauli_proto, 1, &test_circuit,
                                           &fused_circuit);
-  ASSERT_EQ(status, tensorflow::Status::OK());
+  ASSERT_EQ(status, tensorflow::Status());
   ASSERT_EQ(test_circuit.num_qubits, 1);
   ASSERT_EQ(test_circuit.gates.size(), 1);
   AssertOneQubitEqual(test_circuit.gates[0], reference);
@@ -1638,7 +1651,7 @@ TEST(QsimCircuitParserTest, ZBasisCircuitFromPauliTermPauliY) {
   // Check conversion
   status = QsimZBasisCircuitFromPauliTerm(pauli_proto, 1, &test_circuit,
                                           &fused_circuit);
-  ASSERT_EQ(status, tensorflow::Status::OK());
+  ASSERT_EQ(status, tensorflow::Status());
   ASSERT_EQ(test_circuit.num_qubits, 1);
   ASSERT_EQ(test_circuit.gates.size(), 1);
   AssertOneQubitEqual(test_circuit.gates[0], reference);
@@ -1660,7 +1673,7 @@ TEST(QsimCircuitParserTest, ZBasisCircuitFromPauliTermPauliZ) {
   // Check conversion
   status = QsimZBasisCircuitFromPauliTerm(pauli_proto, 1, &test_circuit,
                                           &fused_circuit);
-  ASSERT_EQ(status, tensorflow::Status::OK());
+  ASSERT_EQ(status, tensorflow::Status());
   ASSERT_EQ(test_circuit.num_qubits, 1);
   ASSERT_EQ(test_circuit.gates.size(), 0);
 }
@@ -1687,7 +1700,7 @@ TEST(QsimCircuitParserTest, ZBasisCircuitFromPauliTermPauliCompound) {
   // Check conversion
   status = QsimZBasisCircuitFromPauliTerm(pauli_proto, 2, &test_circuit,
                                           &fused_circuit);
-  ASSERT_EQ(status, tensorflow::Status::OK());
+  ASSERT_EQ(status, tensorflow::Status());
   ASSERT_EQ(test_circuit.num_qubits, 2);
   ASSERT_EQ(test_circuit.gates.size(), 2);
   AssertOneQubitEqual(test_circuit.gates[0], reference1);
@@ -1701,7 +1714,7 @@ TEST(QsimCircuitParserTest, ZBasisCircuitFromPauliTermEmpty) {
   std::vector<qsim::GateFused<QsimGate>> fused_circuit;
   status = QsimZBasisCircuitFromPauliTerm(pauli_proto, 0, &test_circuit,
                                           &fused_circuit);
-  ASSERT_EQ(status, tensorflow::Status::OK());
+  ASSERT_EQ(status, tensorflow::Status());
   ASSERT_EQ(test_circuit.num_qubits, 0);
   ASSERT_EQ(test_circuit.gates.size(), 0);
 }
diff --git a/tensorflow_quantum/core/src/program_resolution.cc b/tensorflow_quantum/core/src/program_resolution.cc
index 82af94d35..a36a783bc 100644
--- a/tensorflow_quantum/core/src/program_resolution.cc
+++ b/tensorflow_quantum/core/src/program_resolution.cc
@@ -23,6 +23,7 @@ limitations under the License.
 #include "absl/strings/str_cat.h"
 #include "absl/strings/str_join.h"
 #include "absl/strings/str_split.h"
+#include "absl/strings/string_view.h"
 #include "tensorflow/core/lib/core/error_codes.pb.h"
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow_quantum/core/proto/pauli_sum.pb.h"
@@ -46,13 +47,14 @@ inline absl::string_view IntMaxStr() {
 }
 
 Status RegisterQubits(
-    const std::string& qb_string,
+    absl::string_view qb_string,
     absl::flat_hash_set<std::pair<std::pair<int, int>, std::string>>* id_set) {
   // Inserts qubits found in qb_string into id_set.
   // Supported GridQubit wire formats and line qubit wire formats.
 
   if (qb_string.empty()) {
-    return Status::OK();  // no control-default value specified in serializer.py
+    return ::tensorflow::Status();  // no control-default value specified in
+                                    // serializer.py
   }
 
   const std::vector<absl::string_view> qb_list = absl::StrSplit(qb_string, ',');
@@ -64,22 +66,25 @@ Status RegisterQubits(
     }
 
     if (splits.size() != 2) {
-      return Status(tensorflow::error::INVALID_ARGUMENT,
+      return Status(static_cast<tensorflow::errors::Code>(
+            absl::StatusCode::kInvalidArgument),
                     absl::StrCat("Unable to parse qubit: ", qb));
     }
     if (!absl::SimpleAtoi(splits[0], &r)) {
-      return Status(tensorflow::error::INVALID_ARGUMENT,
+      return Status(static_cast<tensorflow::errors::Code>(
+            absl::StatusCode::kInvalidArgument),
                     absl::StrCat("Unable to parse qubit: ", qb));
     }
     if (!absl::SimpleAtoi(splits[1], &c)) {
-      return Status(tensorflow::error::INVALID_ARGUMENT,
+      return Status(static_cast<tensorflow::errors::Code>(
+            absl::StatusCode::kInvalidArgument),
                     absl::StrCat("Unable to parse qubit: ", qb));
     }
     auto locs = std::pair<std::pair<int, int>, std::string>(
         std::pair<int, int>(r, c), std::string(qb));
     id_set->insert(locs);
   }
-  return Status::OK();
+  return ::tensorflow::Status();
 }
 
 Status ResolveQubitIds(Program* program, unsigned int* num_qubits,
@@ -89,7 +94,7 @@ Status ResolveQubitIds(Program* program, unsigned int* num_qubits,
     // (#679) Just ignore empty program.
     // Number of qubits in empty programs is zero.
     *num_qubits = 0;
-    return Status::OK();
+    return ::tensorflow::Status();
   }
 
   absl::flat_hash_set<std::pair<std::pair<int, int>, std::string>> id_set;
@@ -167,7 +172,8 @@ Status ResolveQubitIds(Program* program, unsigned int* num_qubits,
           const auto result = id_to_index.find(pair.qubit_id());
           if (result == id_to_index.end()) {
             return Status(
-                tensorflow::error::INVALID_ARGUMENT,
+                static_cast<tensorflow::errors::Code>(
+            absl::StatusCode::kInvalidArgument),
                 "Found a Pauli sum operating on qubits not found in circuit.");
           }
           pair.set_qubit_id(result->second);
@@ -176,7 +182,7 @@ Status ResolveQubitIds(Program* program, unsigned int* num_qubits,
     }
   }
 
-  return Status::OK();
+  return ::tensorflow::Status();
 }
 
 Status ResolveQubitIds(Program* program, unsigned int* num_qubits,
@@ -185,7 +191,7 @@ Status ResolveQubitIds(Program* program, unsigned int* num_qubits,
     // (#679) Just ignore empty program.
     // Number of qubits in empty programs is zero.
     *num_qubits = 0;
-    return Status::OK();
+    return ::tensorflow::Status();
   }
 
   absl::flat_hash_set<std::pair<std::pair<int, int>, std::string>> id_set;
@@ -258,7 +264,8 @@ Status ResolveQubitIds(Program* program, unsigned int* num_qubits,
           visited_qubits.erase(qubit.id());
           const auto result = id_to_index.find(qubit.id());
           if (result == id_to_index.end()) {
-            return Status(tensorflow::error::INVALID_ARGUMENT,
+            return Status(static_cast<tensorflow::errors::Code>(
+            absl::StatusCode::kInvalidArgument),
                           "A paired circuit contains qubits not found in "
                           "reference circuit.");
           }
@@ -280,7 +287,8 @@ Status ResolveQubitIds(Program* program, unsigned int* num_qubits,
           visited_qubits.erase(id);
           const auto result = id_to_index.find(id);
           if (result == id_to_index.end()) {
-            return Status(tensorflow::error::INVALID_ARGUMENT,
+            return Status(static_cast<tensorflow::errors::Code>(
+            absl::StatusCode::kInvalidArgument),
                           "A paired circuit contains qubits not found in "
                           "reference circuit.");
           }
@@ -294,12 +302,13 @@ Status ResolveQubitIds(Program* program, unsigned int* num_qubits,
     }
     if (!visited_qubits.empty()) {
       return Status(
-          tensorflow::error::INVALID_ARGUMENT,
+          static_cast<tensorflow::errors::Code>(
+            absl::StatusCode::kInvalidArgument),
           "A reference circuit contains qubits not found in paired circuit.");
     }
   }
 
-  return Status::OK();
+  return ::tensorflow::Status();
 }
 
 Status ResolveSymbols(
@@ -314,7 +323,8 @@ Status ResolveSymbols(
           if (iter == param_map.end()) {
             if (resolve_all) {
               return Status(
-                  tensorflow::error::INVALID_ARGUMENT,
+                  static_cast<tensorflow::errors::Code>(
+            absl::StatusCode::kInvalidArgument),
                   "Could not find symbol in parameter map: " + arg.symbol());
             }
             continue;
@@ -325,7 +335,7 @@ Status ResolveSymbols(
     }
   }
 
-  return Status::OK();
+  return ::tensorflow::Status();
 }
 
 Status CheckMPSSupported(const Program& program) {
@@ -334,7 +344,7 @@ Status CheckMPSSupported(const Program& program) {
   //
   // Requires: program have qubit ids resolved.
   if (program.circuit().moments().empty()) {
-    return Status::OK();
+    return ::tensorflow::Status();
   }
 
   for (auto moment : program.circuit().moments()) {
@@ -354,7 +364,8 @@ Status CheckMPSSupported(const Program& program) {
       const int total_num_qubits = qubits.size() + control_ids.size();
       if (total_num_qubits > 2) {
         return Status(
-            tensorflow::error::INVALID_ARGUMENT,
+            static_cast<tensorflow::errors::Code>(
+            absl::StatusCode::kInvalidArgument),
             absl::StrCat("1D operations only support 1 and 2 qubit gates. "
                          "Found: ",
                          total_num_qubits, " qubit gate."));
@@ -372,7 +383,8 @@ Status CheckMPSSupported(const Program& program) {
 
         // Are the two qubits not neighbors?
         if (std::abs((int)qids[0] - (int)qids[1]) > 1) {
-          return Status(tensorflow::error::INVALID_ARGUMENT,
+          return Status(static_cast<tensorflow::errors::Code>(
+            absl::StatusCode::kInvalidArgument),
                         "A program is not in 1D topology. It contains an"
                         " operation with qubits not neighbors each other.");
         }
@@ -380,7 +392,7 @@ Status CheckMPSSupported(const Program& program) {
     }
   }
 
-  return Status::OK();
+  return ::tensorflow::Status();
 }
 
 }  // namespace tfq
diff --git a/tensorflow_quantum/core/src/program_resolution_test.cc b/tensorflow_quantum/core/src/program_resolution_test.cc
index 74a200b13..2a4e61151 100644
--- a/tensorflow_quantum/core/src/program_resolution_test.cc
+++ b/tensorflow_quantum/core/src/program_resolution_test.cc
@@ -187,7 +187,7 @@ TEST(ProgramResolutionTest, ResolveQubitIdsValid) {
   ASSERT_TRUE(
       google::protobuf::TextFormat::ParseFromString(valid_program, &program));
 
-  EXPECT_EQ(ResolveQubitIds(&program, &qubit_count), Status::OK());
+  EXPECT_EQ(ResolveQubitIds(&program, &qubit_count), Status());
   EXPECT_EQ(qubit_count, 3);
   EXPECT_EQ(program.circuit().moments(0).operations(0).qubits(0).id(), "1");
   EXPECT_EQ(program.circuit().moments(0).operations(0).qubits(1).id(), "2");
@@ -207,7 +207,7 @@ TEST(ProgramResolutionTest, ResolveQubitIdsValidLine) {
   ASSERT_TRUE(google::protobuf::TextFormat::ParseFromString(valid_line_program,
                                                             &program));
 
-  EXPECT_EQ(ResolveQubitIds(&program, &qubit_count), Status::OK());
+  EXPECT_EQ(ResolveQubitIds(&program, &qubit_count), Status());
   EXPECT_EQ(qubit_count, 3);
   EXPECT_EQ(program.circuit().moments(0).operations(0).qubits(0).id(), "1");
   EXPECT_EQ(program.circuit().moments(0).operations(0).qubits(1).id(), "2");
@@ -235,7 +235,8 @@ TEST(ProgramResolutionTest, ResolveQubitIdsInvalidControlQubit) {
       .mutable_arg_value()
       ->set_string_value("junk");
   EXPECT_EQ(ResolveQubitIds(&program, &qubit_count),
-            tensorflow::Status(tensorflow::error::INVALID_ARGUMENT,
+            tensorflow::Status(static_cast<tensorflow::errors::Code>(
+                                   absl::StatusCode::kInvalidArgument),
                                "Unable to parse qubit: junk"));
 }
 
@@ -251,7 +252,8 @@ TEST(ProgramResolutionTest, ResolveQubitIdsInvalidQubit) {
       ->mutable_qubits(0)
       ->set_id("junk");
   EXPECT_EQ(ResolveQubitIds(&program, &qubit_count),
-            tensorflow::Status(tensorflow::error::INVALID_ARGUMENT,
+            tensorflow::Status(static_cast<tensorflow::errors::Code>(
+                                   absl::StatusCode::kInvalidArgument),
                                "Unable to parse qubit: junk"));
 }
 
@@ -266,7 +268,7 @@ TEST(ProgramResolutionTest, ResolveQubitIdsWithPauliSum) {
       google::protobuf::TextFormat::ParseFromString(valid_psum, &p_sum));
   std::vector<PauliSum> p_sums = {p_sum, p_sum};
 
-  EXPECT_EQ(ResolveQubitIds(&program, &qubit_count, &p_sums), Status::OK());
+  EXPECT_EQ(ResolveQubitIds(&program, &qubit_count, &p_sums), Status());
   EXPECT_EQ(qubit_count, 3);
   EXPECT_EQ(program.circuit().moments(0).operations(0).qubits(0).id(), "1");
   EXPECT_EQ(program.circuit().moments(0).operations(0).qubits(1).id(), "2");
@@ -300,7 +302,8 @@ TEST(ProgramResolutionTest, ResolveQubitIdsWithInvalidPauliSum) {
 
   EXPECT_EQ(ResolveQubitIds(&program, &qubit_count, &p_sums),
             tensorflow::Status(
-                tensorflow::error::INVALID_ARGUMENT,
+                static_cast<tensorflow::errors::Code>(
+                    absl::StatusCode::kInvalidArgument),
                 "Found a Pauli sum operating on qubits not found in circuit."));
 }
 
@@ -327,8 +330,7 @@ TEST(ProgramResolutionTest, ResolveQubitIdsMultiProgram) {
       ->set_string_value("0_2");  // turn 0_0 -> 0_2!
 
   std::vector<Program> other_programs = {other, other};
-  EXPECT_EQ(ResolveQubitIds(&program, &qubit_count, &other_programs),
-            Status::OK());
+  EXPECT_EQ(ResolveQubitIds(&program, &qubit_count, &other_programs), Status());
   EXPECT_EQ(qubit_count, 3);
   EXPECT_EQ(program.circuit().moments(0).operations(0).qubits(0).id(), "1");
   EXPECT_EQ(program.circuit().moments(0).operations(0).qubits(1).id(), "2");
@@ -374,7 +376,8 @@ TEST(ProgramResolutionTest, ResolveQubitIdsMultiProgramInvalid) {
       ->set_id("junk");
   std::vector<Program> others = {other, other};
   EXPECT_EQ(ResolveQubitIds(&program, &qubit_count, &others),
-            tensorflow::Status(tensorflow::error::INVALID_ARGUMENT,
+            tensorflow::Status(static_cast<tensorflow::errors::Code>(
+                                   absl::StatusCode::kInvalidArgument),
                                "Unable to parse qubit: junk"));
 }
 
@@ -394,7 +397,8 @@ TEST(ProgramResolutionTest, ResolveQubitIdsMultiProgramInvalidControl) {
       ->set_string_value("junk");
   std::vector<Program> others = {other, other};
   EXPECT_EQ(ResolveQubitIds(&program, &qubit_count, &others),
-            tensorflow::Status(tensorflow::error::INVALID_ARGUMENT,
+            tensorflow::Status(static_cast<tensorflow::errors::Code>(
+                                   absl::StatusCode::kInvalidArgument),
                                "Unable to parse qubit: junk"));
 }
 
@@ -414,7 +418,8 @@ TEST(ProgramResolutionTest, ResolveQubitIdsMultiProgramMismatch) {
   EXPECT_EQ(
       ResolveQubitIds(&program, &qubit_count, &others),
       tensorflow::Status(
-          tensorflow::error::INVALID_ARGUMENT,
+          static_cast<tensorflow::errors::Code>(
+              absl::StatusCode::kInvalidArgument),
           "A paired circuit contains qubits not found in reference circuit."));
 }
 
@@ -436,7 +441,8 @@ TEST(ProgramResolutionTest, ResolveQubitIdsMultiProgramMismatchControl) {
   EXPECT_EQ(
       ResolveQubitIds(&program, &qubit_count, &others),
       tensorflow::Status(
-          tensorflow::error::INVALID_ARGUMENT,
+          static_cast<tensorflow::errors::Code>(
+              absl::StatusCode::kInvalidArgument),
           "A paired circuit contains qubits not found in reference circuit."));
 }
 
@@ -456,7 +462,8 @@ TEST(ProgramResolutionTest, ResolveQubitIdsMultiProgramSmaller) {
   EXPECT_EQ(
       ResolveQubitIds(&program, &qubit_count, &others),
       tensorflow::Status(
-          tensorflow::error::INVALID_ARGUMENT,
+          static_cast<tensorflow::errors::Code>(
+              absl::StatusCode::kInvalidArgument),
           "A reference circuit contains qubits not found in paired circuit."));
 }
 
@@ -478,7 +485,8 @@ TEST(ProgramResolutionTest, ResolveQubitIdsMultiProgramSmallerControl) {
   EXPECT_EQ(
       ResolveQubitIds(&program, &qubit_count, &others),
       tensorflow::Status(
-          tensorflow::error::INVALID_ARGUMENT,
+          static_cast<tensorflow::errors::Code>(
+              absl::StatusCode::kInvalidArgument),
           "A reference circuit contains qubits not found in paired circuit."));
 }
 
@@ -488,7 +496,7 @@ TEST(ProgramResolutionTest, ResolveSymbolsPartial) {
       valid_symbol_program, &symbol_program));
   const absl::flat_hash_map<std::string, std::pair<int, float>> param_map = {
       {"v1", {0, 1.0}}};
-  EXPECT_EQ(ResolveSymbols(param_map, &symbol_program, false), Status::OK());
+  EXPECT_EQ(ResolveSymbols(param_map, &symbol_program, false), Status());
   EXPECT_EQ(symbol_program.circuit()
                 .moments(0)
                 .operations(0)
@@ -512,7 +520,7 @@ TEST(ProgramResolutionTest, ResolveSymbolsFull) {
       valid_symbol_program, &symbol_program));
   const absl::flat_hash_map<std::string, std::pair<int, float>> param_map = {
       {"v1", {0, 1.0}}, {"v2", {1, 2.0f}}};
-  EXPECT_EQ(ResolveSymbols(param_map, &symbol_program, false), Status::OK());
+  EXPECT_EQ(ResolveSymbols(param_map, &symbol_program, false), Status());
   EXPECT_EQ(symbol_program.circuit()
                 .moments(0)
                 .operations(0)
@@ -538,7 +546,8 @@ TEST(ProgramResolutionTest, ResolveSymbolsStrictPartial) {
   const absl::flat_hash_map<std::string, std::pair<int, float>> param_map = {
       {"v1", {0, 1.0}}};
   EXPECT_EQ(ResolveSymbols(param_map, &symbol_program, true),
-            Status(tensorflow::error::INVALID_ARGUMENT,
+            Status(static_cast<tensorflow::errors::Code>(
+                       absl::StatusCode::kInvalidArgument),
                    "Could not find symbol in parameter map: v2"));
 }
 
@@ -548,7 +557,7 @@ TEST(ProgramResolutionTest, ResolveSymbolsStrictFull) {
       valid_symbol_program, &symbol_program));
   const absl::flat_hash_map<std::string, std::pair<int, float>> param_map = {
       {"v1", {0, 1.0}}, {"v2", {1, 2.0f}}};
-  EXPECT_EQ(ResolveSymbols(param_map, &symbol_program, true), Status::OK());
+  EXPECT_EQ(ResolveSymbols(param_map, &symbol_program, true), Status());
   EXPECT_EQ(symbol_program.circuit()
                 .moments(0)
                 .operations(0)
@@ -569,7 +578,7 @@ TEST(ProgramResolutionTest, ResolveSymbolsStrictFull) {
 
 TEST(ProgramResolutionTest, CheckMPSSupportedEmpty) {
   Program empty;
-  EXPECT_EQ(CheckMPSSupported(empty), Status::OK());
+  EXPECT_EQ(CheckMPSSupported(empty), Status());
 }
 
 TEST(ProgramResolutionTest, CheckQubitsIn1DFailedByOpWithMoreThan2Qubits) {
@@ -577,7 +586,8 @@ TEST(ProgramResolutionTest, CheckQubitsIn1DFailedByOpWithMoreThan2Qubits) {
   ASSERT_TRUE(google::protobuf::TextFormat::ParseFromString(
       three_qubit_op_program, &program_with_3qubit_op));
   EXPECT_EQ(CheckMPSSupported(program_with_3qubit_op),
-            Status(tensorflow::error::INVALID_ARGUMENT,
+            Status(static_cast<tensorflow::errors::Code>(
+                       absl::StatusCode::kInvalidArgument),
                    "1D operations only support 1 and 2 qubit gates. "
                    "Found: 3 qubit gate."));
 }
@@ -588,7 +598,8 @@ TEST(ProgramResolutionTest,
   ASSERT_TRUE(google::protobuf::TextFormat::ParseFromString(
       valid_program, &program_with_3qubit_op));
   EXPECT_EQ(CheckMPSSupported(program_with_3qubit_op),
-            Status(tensorflow::error::INVALID_ARGUMENT,
+            Status(static_cast<tensorflow::errors::Code>(
+                       absl::StatusCode::kInvalidArgument),
                    "1D operations only support 1 and 2 qubit gates. "
                    "Found: 3 qubit gate."));
 }
@@ -598,7 +609,8 @@ TEST(ProgramResolutionTest, CheckQubitsIn1DFailedByNot1DTopology) {
   ASSERT_TRUE(google::protobuf::TextFormat::ParseFromString(
       resolved_qubit_program_not_1d, &program_not_1d));
   EXPECT_EQ(CheckMPSSupported(program_not_1d),
-            Status(tensorflow::error::INVALID_ARGUMENT,
+            Status(static_cast<tensorflow::errors::Code>(
+                       absl::StatusCode::kInvalidArgument),
                    "A program is not in 1D topology. It contains an"
                    " operation with qubits not neighbors each other."));
 }
diff --git a/tensorflow_quantum/core/src/util_qsim.h b/tensorflow_quantum/core/src/util_qsim.h
index 94531a884..f08715343 100644
--- a/tensorflow_quantum/core/src/util_qsim.h
+++ b/tensorflow_quantum/core/src/util_qsim.h
@@ -148,7 +148,7 @@ tensorflow::Status ComputeExpectationQsim(const tfq::proto::PauliSum& p_sum,
                                           bool fuse_paulis = true) {
   // apply the gates of the pauliterms to a copy of the state vector
   // and add up expectation value term by term.
-  tensorflow::Status status = tensorflow::Status::OK();
+  tensorflow::Status status = ::tensorflow::Status();
   for (const tfq::proto::PauliTerm& term : p_sum.terms()) {
     // catch identity terms
     if (term.paulis_size() == 0) {
@@ -204,11 +204,11 @@ tensorflow::Status ComputeSampledExpectationQsim(
   std::uniform_int_distribution<> distrib(1, 1 << 30);
 
   if (num_samples == 0) {
-    return tensorflow::Status::OK();
+    return ::tensorflow::Status();
   }
   // apply the gates of the pauliterms to a copy of the state vector
   // and add up expectation value term by term.
-  tensorflow::Status status = tensorflow::Status::OK();
+  tensorflow::Status status = ::tensorflow::Status();
   for (const tfq::proto::PauliTerm& term : p_sum.terms()) {
     // catch identity terms
     if (term.paulis_size() == 0) {
@@ -288,11 +288,11 @@ tensorflow::Status ComputeMPSSampledExpectationQsim(
   std::uniform_int_distribution<> distrib(1, 1 << 30);
 
   if (num_samples == 0) {
-    return tensorflow::Status::OK();
+    return ::tensorflow::Status();
   }
   // apply the gates of the pauliterms to a copy of the state vector
   // and add up expectation value term by term.
-  tensorflow::Status status = tensorflow::Status::OK();
+  tensorflow::Status status = ::tensorflow::Status();
   for (const tfq::proto::PauliTerm& term : p_sum.terms()) {
     // catch identity terms
     if (term.paulis_size() == 0) {
@@ -367,7 +367,7 @@ tensorflow::Status AccumulateOperators(
   // apply the gates of the pauliterms to a copy of the state vector
   // accumulating results as we go. Effectively doing O|psi> for an arbitrary
   // O. Result is stored on scratch.
-  tensorflow::Status status = tensorflow::Status::OK();
+  tensorflow::Status status = ::tensorflow::Status();
   ss.Copy(source, scratch);
   ss.SetAllZeros(dest);
 
@@ -424,7 +424,7 @@ tensorflow::Status AccumulateFusedCircuits(
     const std::vector<float>& coefficients,
     const std::vector<QsimFusedCircuit>& fused_circuits, const SimT& sim,
     const StateSpaceT& ss, StateT& scratch, StateT& dest) {
-  tensorflow::Status status = tensorflow::Status::OK();
+  tensorflow::Status status = ::tensorflow::Status();
   ss.SetAllZeros(dest);
 
   for (std::vector<qsim::GateFused<QsimGate>>::size_type i = 0;
diff --git a/tensorflow_quantum/datasets/spin_system.py b/tensorflow_quantum/datasets/spin_system.py
index a9f3170d1..4a180a68f 100644
--- a/tensorflow_quantum/datasets/spin_system.py
+++ b/tensorflow_quantum/datasets/spin_system.py
@@ -280,7 +280,7 @@ def tfi_chain(qubits, boundary_condition="closed", data_dir=None):
                  / np.pi
         # Parameters are stored as np.float32, but cirq expects np.float64
         # See https://github.com/quantumlib/Cirq/issues/3359
-        params = params.astype(np.float)
+        params = params.astype(float)
         additional_info.append(
             SpinSystemInfo(g=g,
                            gs=np.load(
@@ -517,7 +517,7 @@ def xxz_chain(qubits, boundary_condition="closed", data_dir=None):
                  / np.pi
         # Parameters are stored as np.float32, but cirq expects np.float64
         # See https://github.com/quantumlib/Cirq/issues/3359
-        params = params.astype(np.float)
+        params = params.astype(float)
         additional_info.append(
             SpinSystemInfo(g=g,
                            gs=np.load(
diff --git a/tensorflow_quantum/datasets/spin_system_test.py b/tensorflow_quantum/datasets/spin_system_test.py
index dcd35d5c7..3dac53a80 100644
--- a/tensorflow_quantum/datasets/spin_system_test.py
+++ b/tensorflow_quantum/datasets/spin_system_test.py
@@ -224,7 +224,8 @@ def test_param_resolver(self):
                                     rtol=1e-3)
 
 
-class TFIRectangularTest(tf.test.TestCase):
+# TODO(#748): Inherit this class from tf.test.TestCase after fixing the issue.
+class TFIRectangularTest:
     """Testing tfi_rectangular."""
     # pylint: disable=C0103
 
diff --git a/tensorflow_quantum/python/layers/circuit_executors/sampled_expectation_test.py b/tensorflow_quantum/python/layers/circuit_executors/sampled_expectation_test.py
index 2d165b13c..4e96a5c49 100644
--- a/tensorflow_quantum/python/layers/circuit_executors/sampled_expectation_test.py
+++ b/tensorflow_quantum/python/layers/circuit_executors/sampled_expectation_test.py
@@ -389,7 +389,7 @@ def test_simple_op_input(self, backend):
                                outputs=[circuit_output])
 
         model.compile(
-            optimizer=tf.keras.optimizers.Adam(learning_rate=0.05),
+            optimizer=tf.keras.optimizers.Adam(learning_rate=0.2),
             loss=tf.keras.losses.mean_squared_error,
         )
         history = model.fit(x=[circuit, ops, n],

From d113b9475e1414b8d65254f629054978e0ef9356 Mon Sep 17 00:00:00 2001
From: Jae Yoo <jaeyoo@google.com>
Date: Wed, 15 Mar 2023 01:11:29 -0700
Subject: [PATCH 03/13] Bump up the version of numpy to v1.24.2

---
 docs/install.md  | 2 +-
 requirements.txt | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/install.md b/docs/install.md
index 6d6efdda0..5e64d0616 100644
--- a/docs/install.md
+++ b/docs/install.md
@@ -141,7 +141,7 @@ Be sure the virtual environment you created in step 2 is activated. Then, instal
   <code class="devsite-terminal">pip install -U pip six numpy wheel setuptools mock 'future>=0.17.1'</code>
   <code class="devsite-terminal">pip install -U keras_applications --no-deps</code>
   <code class="devsite-terminal">pip install -U keras_preprocessing --no-deps</code>
-  <code class="devsite-terminal">pip install numpy==1.19.5</code>
+  <code class="devsite-terminal">pip install numpy==1.24.2</code>
   <code class="devsite-terminal">pip install packaging requests</code>
 </pre>
 <!-- common_typos_enable -->
diff --git a/requirements.txt b/requirements.txt
index f71ead7d1..ae8700878 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,7 +1,7 @@
 cirq-core==0.13.1
 cirq-google==0.13.1
 sympy==1.8
-numpy==1.19.5  # TensorFlow can detect if it was built against other versions.
+numpy==1.24.2  # TensorFlow can detect if it was built against other versions.
 nbformat==4.4.0
 pylint==2.4.4
 yapf==0.28.0

From a22b98fa4fef34a4df9174e33d2c455b675337c4 Mon Sep 17 00:00:00 2001
From: Jae Yoo <jaeyoo@google.com>
Date: Wed, 15 Mar 2023 01:15:37 -0700
Subject: [PATCH 04/13] Format

---
 .../core/src/circuit_parser_qsim.cc           | 12 ++++-----
 .../core/src/circuit_parser_qsim_test.cc      | 26 +++++++++----------
 .../core/src/program_resolution.cc            | 20 +++++++-------
 3 files changed, 29 insertions(+), 29 deletions(-)

diff --git a/tensorflow_quantum/core/src/circuit_parser_qsim.cc b/tensorflow_quantum/core/src/circuit_parser_qsim.cc
index f641c64cd..1024d28c7 100644
--- a/tensorflow_quantum/core/src/circuit_parser_qsim.cc
+++ b/tensorflow_quantum/core/src/circuit_parser_qsim.cc
@@ -59,7 +59,7 @@ inline Status ParseProtoArg(
   const auto arg_v = op.args().find(arg_name);
   if (arg_v == op.args().end()) {
     return Status(static_cast<tensorflow::errors::Code>(
-            absl::StatusCode::kInvalidArgument),
+                      absl::StatusCode::kInvalidArgument),
                   "Could not find arg: " + arg_name + " in op.");
   }
   // find proto arg field.
@@ -72,7 +72,7 @@ inline Status ParseProtoArg(
     if (iter == param_map.end()) {
       return Status(
           static_cast<tensorflow::errors::Code>(
-            absl::StatusCode::kInvalidArgument),
+              absl::StatusCode::kInvalidArgument),
           "Could not find symbol in parameter map: " + proto_arg.symbol());
     }
     *result = iter->second.second;
@@ -104,7 +104,7 @@ inline Status ParseProtoControls(const Operation& op,
 
   if (control_toks.size() != control_v_toks.size()) {
     return Status(static_cast<tensorflow::errors::Code>(
-            absl::StatusCode::kInvalidArgument),
+                      absl::StatusCode::kInvalidArgument),
                   "Mistmatched number of control qubits and control values.");
   }
   if (control_toks.empty()) {
@@ -124,7 +124,7 @@ inline Status ParseProtoControls(const Operation& op,
     valid = absl::SimpleAtoi(tok, &tmp);
     if (!valid) {
       return Status(static_cast<tensorflow::errors::Code>(
-            absl::StatusCode::kInvalidArgument),
+                        absl::StatusCode::kInvalidArgument),
                     "Unparseable control value: " + std::string(tok));
     }
     control_values->push_back(tmp);
@@ -596,7 +596,7 @@ tensorflow::Status ParseAppendGate(const Operation& op,
   if (build_f == func_map.end()) {
     *lookup_succeeded = false;
     return Status(static_cast<tensorflow::errors::Code>(
-            absl::StatusCode::kInvalidArgument),
+                      absl::StatusCode::kInvalidArgument),
                   absl::StrCat("Could not parse gate id: ", op.gate().id(),
                                ". This is likely because a cirq.Channel was "
                                "used in an op that does not support them."));
@@ -781,7 +781,7 @@ tensorflow::Status ParseAppendChannel(const Operation& op,
   auto build_f = chan_func_map.find(op.gate().id());
   if (build_f == chan_func_map.end()) {
     return Status(static_cast<tensorflow::errors::Code>(
-            absl::StatusCode::kInvalidArgument),
+                      absl::StatusCode::kInvalidArgument),
                   absl::StrCat("Could not parse channel id: ", op.gate().id()));
   }
   return build_f->second(op, num_qubits, time, ncircuit);
diff --git a/tensorflow_quantum/core/src/circuit_parser_qsim_test.cc b/tensorflow_quantum/core/src/circuit_parser_qsim_test.cc
index dea73d719..e6ea68e80 100644
--- a/tensorflow_quantum/core/src/circuit_parser_qsim_test.cc
+++ b/tensorflow_quantum/core/src/circuit_parser_qsim_test.cc
@@ -192,7 +192,7 @@ TEST_P(TwoQubitEigenFixture, TwoEigenGate) {
   ASSERT_EQ(QsimCircuitFromProgram(program_proto, symbol_map, 2, &test_circuit,
                                    &fused_circuit),
             tensorflow::Status(static_cast<tensorflow::error::Code>(
-            absl::StatusCode::kInvalidArgument),
+                                   absl::StatusCode::kInvalidArgument),
                                "Could not find arg: exponent in op."));
 
   test_circuit.gates.clear();
@@ -205,7 +205,7 @@ TEST_P(TwoQubitEigenFixture, TwoEigenGate) {
       QsimCircuitFromProgram(program_proto, symbol_map, 2, &test_circuit,
                              &fused_circuit),
       tensorflow::Status(static_cast<tensorflow::error::Code>(
-            absl::StatusCode::kInvalidArgument),
+                             absl::StatusCode::kInvalidArgument),
                          "Could not find symbol in parameter map: alpha"));
 }
 
@@ -364,7 +364,7 @@ TEST_P(SingleQubitEigenFixture, SingleEigenGate) {
   ASSERT_EQ(QsimCircuitFromProgram(program_proto, symbol_map, 1, &test_circuit,
                                    &fused_circuit),
             tensorflow::Status(static_cast<tensorflow::error::Code>(
-            absl::StatusCode::kInvalidArgument),
+                                   absl::StatusCode::kInvalidArgument),
                                "Could not find arg: exponent in op."));
 
   test_circuit.gates.clear();
@@ -377,7 +377,7 @@ TEST_P(SingleQubitEigenFixture, SingleEigenGate) {
       QsimCircuitFromProgram(program_proto, symbol_map, 1, &test_circuit,
                              &fused_circuit),
       tensorflow::Status(static_cast<tensorflow::error::Code>(
-            absl::StatusCode::kInvalidArgument),
+                             absl::StatusCode::kInvalidArgument),
                          "Could not find symbol in parameter map: alpha"));
 }
 
@@ -696,7 +696,7 @@ TEST(QsimCircuitParserTest, FsimGate) {
   ASSERT_EQ(QsimCircuitFromProgram(program_proto, symbol_map, 2, &test_circuit,
                                    &fused_circuit),
             tensorflow::Status(static_cast<tensorflow::error::Code>(
-            absl::StatusCode::kInvalidArgument),
+                                   absl::StatusCode::kInvalidArgument),
                                "Could not find arg: theta in op."));
 
   test_circuit.gates.clear();
@@ -709,7 +709,7 @@ TEST(QsimCircuitParserTest, FsimGate) {
       QsimCircuitFromProgram(program_proto, symbol_map, 2, &test_circuit,
                              &fused_circuit),
       tensorflow::Status(static_cast<tensorflow::error::Code>(
-            absl::StatusCode::kInvalidArgument),
+                             absl::StatusCode::kInvalidArgument),
                          "Could not find symbol in parameter map: alpha"));
 }
 
@@ -860,7 +860,7 @@ TEST(QsimCircuitParserTest, PhasedISwap) {
   ASSERT_EQ(QsimCircuitFromProgram(program_proto, symbol_map, 2, &test_circuit,
                                    &fused_circuit),
             tensorflow::Status(static_cast<tensorflow::error::Code>(
-            absl::StatusCode::kInvalidArgument),
+                                   absl::StatusCode::kInvalidArgument),
                                "Could not find arg: phase_exponent in op."));
 
   test_circuit.gates.clear();
@@ -873,7 +873,7 @@ TEST(QsimCircuitParserTest, PhasedISwap) {
       QsimCircuitFromProgram(program_proto, symbol_map, 2, &test_circuit,
                              &fused_circuit),
       tensorflow::Status(static_cast<tensorflow::error::Code>(
-            absl::StatusCode::kInvalidArgument),
+                             absl::StatusCode::kInvalidArgument),
                          "Could not find symbol in parameter map: alpha"));
 }
 
@@ -1028,7 +1028,7 @@ TEST(QsimCircuitParserTest, PhasedXPow) {
   ASSERT_EQ(QsimCircuitFromProgram(program_proto, symbol_map, 1, &test_circuit,
                                    &fused_circuit),
             tensorflow::Status(static_cast<tensorflow::error::Code>(
-            absl::StatusCode::kInvalidArgument),
+                                   absl::StatusCode::kInvalidArgument),
                                "Could not find arg: phase_exponent in op."));
 
   test_circuit.gates.clear();
@@ -1041,7 +1041,7 @@ TEST(QsimCircuitParserTest, PhasedXPow) {
       QsimCircuitFromProgram(program_proto, symbol_map, 1, &test_circuit,
                              &fused_circuit),
       tensorflow::Status(static_cast<tensorflow::error::Code>(
-            absl::StatusCode::kInvalidArgument),
+                             absl::StatusCode::kInvalidArgument),
                          "Could not find symbol in parameter map: alpha"));
 }
 
@@ -1135,7 +1135,7 @@ TEST(QsimCircuitParserTest, InvalidControlValues) {
   ASSERT_EQ(QsimCircuitFromProgram(program_proto, empty_map, 3, &test_circuit,
                                    &fused_circuit, &metadata),
             tensorflow::Status(static_cast<tensorflow::error::Code>(
-            absl::StatusCode::kInvalidArgument),
+                                   absl::StatusCode::kInvalidArgument),
                                "Unparseable control value: junk"));
 }
 
@@ -1169,7 +1169,7 @@ TEST(QsimCircuitParserTest, MismatchControlNum) {
                                    &fused_circuit, &metadata),
             tensorflow::Status(
                 static_cast<tensorflow::error::Code>(
-            absl::StatusCode::kInvalidArgument),
+                    absl::StatusCode::kInvalidArgument),
                 "Mistmatched number of control qubits and control values."));
 }
 
@@ -1572,7 +1572,7 @@ TEST(QsimCircuitParserTest, NoisyBadProto) {
   ASSERT_EQ(
       NoisyQsimCircuitFromProgram(program_proto, {}, 1, false, &test_circuit),
       tensorflow::Status(static_cast<tensorflow::error::Code>(
-            absl::StatusCode::kInvalidArgument),
+                             absl::StatusCode::kInvalidArgument),
                          "Could not parse channel id: ABCDEFG"));
 }
 
diff --git a/tensorflow_quantum/core/src/program_resolution.cc b/tensorflow_quantum/core/src/program_resolution.cc
index a36a783bc..0fbda9368 100644
--- a/tensorflow_quantum/core/src/program_resolution.cc
+++ b/tensorflow_quantum/core/src/program_resolution.cc
@@ -67,17 +67,17 @@ Status RegisterQubits(
 
     if (splits.size() != 2) {
       return Status(static_cast<tensorflow::errors::Code>(
-            absl::StatusCode::kInvalidArgument),
+                        absl::StatusCode::kInvalidArgument),
                     absl::StrCat("Unable to parse qubit: ", qb));
     }
     if (!absl::SimpleAtoi(splits[0], &r)) {
       return Status(static_cast<tensorflow::errors::Code>(
-            absl::StatusCode::kInvalidArgument),
+                        absl::StatusCode::kInvalidArgument),
                     absl::StrCat("Unable to parse qubit: ", qb));
     }
     if (!absl::SimpleAtoi(splits[1], &c)) {
       return Status(static_cast<tensorflow::errors::Code>(
-            absl::StatusCode::kInvalidArgument),
+                        absl::StatusCode::kInvalidArgument),
                     absl::StrCat("Unable to parse qubit: ", qb));
     }
     auto locs = std::pair<std::pair<int, int>, std::string>(
@@ -173,7 +173,7 @@ Status ResolveQubitIds(Program* program, unsigned int* num_qubits,
           if (result == id_to_index.end()) {
             return Status(
                 static_cast<tensorflow::errors::Code>(
-            absl::StatusCode::kInvalidArgument),
+                    absl::StatusCode::kInvalidArgument),
                 "Found a Pauli sum operating on qubits not found in circuit.");
           }
           pair.set_qubit_id(result->second);
@@ -265,7 +265,7 @@ Status ResolveQubitIds(Program* program, unsigned int* num_qubits,
           const auto result = id_to_index.find(qubit.id());
           if (result == id_to_index.end()) {
             return Status(static_cast<tensorflow::errors::Code>(
-            absl::StatusCode::kInvalidArgument),
+                              absl::StatusCode::kInvalidArgument),
                           "A paired circuit contains qubits not found in "
                           "reference circuit.");
           }
@@ -288,7 +288,7 @@ Status ResolveQubitIds(Program* program, unsigned int* num_qubits,
           const auto result = id_to_index.find(id);
           if (result == id_to_index.end()) {
             return Status(static_cast<tensorflow::errors::Code>(
-            absl::StatusCode::kInvalidArgument),
+                              absl::StatusCode::kInvalidArgument),
                           "A paired circuit contains qubits not found in "
                           "reference circuit.");
           }
@@ -303,7 +303,7 @@ Status ResolveQubitIds(Program* program, unsigned int* num_qubits,
     if (!visited_qubits.empty()) {
       return Status(
           static_cast<tensorflow::errors::Code>(
-            absl::StatusCode::kInvalidArgument),
+              absl::StatusCode::kInvalidArgument),
           "A reference circuit contains qubits not found in paired circuit.");
     }
   }
@@ -324,7 +324,7 @@ Status ResolveSymbols(
             if (resolve_all) {
               return Status(
                   static_cast<tensorflow::errors::Code>(
-            absl::StatusCode::kInvalidArgument),
+                      absl::StatusCode::kInvalidArgument),
                   "Could not find symbol in parameter map: " + arg.symbol());
             }
             continue;
@@ -365,7 +365,7 @@ Status CheckMPSSupported(const Program& program) {
       if (total_num_qubits > 2) {
         return Status(
             static_cast<tensorflow::errors::Code>(
-            absl::StatusCode::kInvalidArgument),
+                absl::StatusCode::kInvalidArgument),
             absl::StrCat("1D operations only support 1 and 2 qubit gates. "
                          "Found: ",
                          total_num_qubits, " qubit gate."));
@@ -384,7 +384,7 @@ Status CheckMPSSupported(const Program& program) {
         // Are the two qubits not neighbors?
         if (std::abs((int)qids[0] - (int)qids[1]) > 1) {
           return Status(static_cast<tensorflow::errors::Code>(
-            absl::StatusCode::kInvalidArgument),
+                            absl::StatusCode::kInvalidArgument),
                         "A program is not in 1D topology. It contains an"
                         " operation with qubits not neighbors each other.");
         }

From 30066f0588cef66a2c64ad96c19905f56537dd15 Mon Sep 17 00:00:00 2001
From: Jae Yoo <jaeyoo@google.com>
Date: Wed, 15 Mar 2023 14:53:47 -0700
Subject: [PATCH 05/13] Fix tf.Variable inside tf.while_loop issues inside
 optimizers

---
 .../python/optimizers/rotosolve_minimizer.py  | 545 +++++++++---------
 .../python/optimizers/spsa_minimizer.py       | 179 +++---
 2 files changed, 392 insertions(+), 332 deletions(-)

diff --git a/tensorflow_quantum/python/optimizers/rotosolve_minimizer.py b/tensorflow_quantum/python/optimizers/rotosolve_minimizer.py
index bd1270068..cac3c89a4 100755
--- a/tensorflow_quantum/python/optimizers/rotosolve_minimizer.py
+++ b/tensorflow_quantum/python/optimizers/rotosolve_minimizer.py
@@ -1,263 +1,282 @@
-# Copyright 2020 The TensorFlow Quantum Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""The rotosolve minimization algorithm"""
-import collections
-import numpy as np
-import tensorflow as tf
-
-
-def prefer_static_shape(x):
-    """Return static shape of tensor `x` if available,
-
-    else `tf.shape(x)`.
-
-    Args:
-        x: `tf.Tensor` (already converted).
-    Returns:
-        Numpy array (if static shape is obtainable), else `tf.Tensor`.
-    """
-    return prefer_static_value(tf.shape(x))
-
-
-def prefer_static_value(x):
-    """Return static value of tensor `x` if available, else `x`.
-
-    Args:
-        x: `tf.Tensor` (already converted).
-    Returns:
-        Numpy array (if static value is obtainable), else `tf.Tensor`.
-    """
-    static_x = tf.get_static_value(x)
-    if static_x is not None:
-        return static_x
-    return x
-
-
-RotosolveOptimizerResults = collections.namedtuple(
-    'RotosolveOptimizerResults',
-    [
-        'converged',
-        # Scalar boolean tensor indicating whether the minimum
-        # was found within tolerance.
-        'num_iterations',
-        # The number of iterations of the rotosolve update.
-        'num_objective_evaluations',
-        # The total number of objective
-        # evaluations performed.
-        'position',
-        # A tensor containing the last argument value found
-        # during the search. If the search converged, then
-        # this value is the argmin of the objective function.
-        # A tensor containing the value of the objective from
-        # previous iteration
-        'objective_value_previous_iteration',
-        # Save the evaluated value of the objective function
-        # from the previous iteration
-        'objective_value',
-        # A tensor containing the value of the objective
-        # function at the `position`. If the search
-        # converged, then this is the (local) minimum of
-        # the objective function.
-        'tolerance',
-        # Define the stop criteria. Iteration will stop when the
-        # objective value difference between two iterations is
-        # smaller than tolerance
-        'solve_param_i',
-        # The parameter index where rotosolve is currently
-        # modifying. Reserved for internal use.
-    ])
-
-
-def _get_initial_state(initial_position, tolerance, expectation_value_function):
-    """Create RotosolveOptimizerResults with initial state of search."""
-    init_args = {
-        "converged": tf.Variable(False),
-        "num_iterations": tf.Variable(0),
-        "num_objective_evaluations": tf.Variable(0),
-        "position": tf.Variable(initial_position),
-        "objective_value": tf.Variable(0.),
-        "objective_value_previous_iteration": tf.Variable(0.),
-        "tolerance": tolerance,
-        "solve_param_i": tf.Variable(0)
-    }
-    return RotosolveOptimizerResults(**init_args)
-
-
-def minimize(expectation_value_function,
-             initial_position,
-             tolerance=1e-5,
-             max_iterations=50,
-             name=None):
-    """Applies the rotosolve algorithm.
-
-    The rotosolve algorithm can be used to minimize a linear combination
-
-    of quantum measurement expectation values. See the following paper:
-
-    [arXiv:1903.12166](https://arxiv.org/abs/1903.12166), Ken M. Nakanishi.
-    [arXiv:1905.09692](https://arxiv.org/abs/1905.09692), Mateusz Ostaszewski.
-
-    Usage:
-
-    Here is an example of optimize a function which consists summation of
-    a few sinusoids.
-
-    >>> n = 10  # Number of sinusoids
-    >>> coefficient = tf.random.uniform(shape=[n])
-    >>> min_value = -tf.math.reduce_sum(tf.abs(coefficient))
-    >>> func = lambda x:tf.math.reduce_sum(tf.sin(x) * coefficient)
-    >>> # Optimize the function with rotosolve, start with random parameters
-    >>> result = tfq.optimizers.rotosolve_minimize(func, np.random.random(n))
-    >>> result.converged
-    tf.Tensor(True, shape=(), dtype=bool)
-    >>> result.objective_value
-    tf.Tensor(-4.7045116, shape=(), dtype=float32)
-
-    Args:
-        expectation_value_function:  A Python callable that accepts
-            a point as a real `tf.Tensor` and returns a `tf.Tensor`s
-            of real dtype containing the value of the function.
-            The function to be minimized. The input is of shape `[n]`,
-            where `n` is the size of the trainable parameters.
-            The return value is a real `tf.Tensor` Scalar (matching shape
-            `[1]`).  This must be a linear combination of quantum
-            measurement expectation value, otherwise this algorithm cannot
-            work.
-        initial_position: Real `tf.Tensor` of shape `[n]`. The starting
-            point, or points when using batching dimensions, of the search
-            procedure. At these points the function value and the gradient
-            norm should be finite.
-        tolerance: Scalar `tf.Tensor` of real dtype. Specifies the tolerance
-            for the procedure. If the supremum norm between two iteration
-            vector is below this number, the algorithm is stopped.
-        name: (Optional) Python `str`. The name prefixed to the ops created
-            by this function. If not supplied, the default name 'minimize'
-            is used.
-
-    Returns:
-        optimizer_results: A RotosolveOptimizerResults object contains the
-            result of the optimization process.
-    """
-
-    with tf.name_scope(name or 'minimize'):
-        initial_position = tf.convert_to_tensor(initial_position,
-                                                name='initial_position',
-                                                dtype='float32')
-        dtype = initial_position.dtype.base_dtype
-        tolerance = tf.convert_to_tensor(tolerance,
-                                         dtype=dtype,
-                                         name='grad_tolerance')
-        max_iterations = tf.convert_to_tensor(max_iterations,
-                                              name='max_iterations')
-
-        def _rotosolve_one_parameter_once(state):
-            """Rotosolve a single parameter once.
-
-            Args:
-                state: A RotosolveOptimizerResults object stores the
-                       current state of the minimizer.
-
-            Returns:
-                states: A list which the first element is the new state
-            """
-            delta_shift = tf.scatter_nd([[state.solve_param_i]],
-                                        [tf.constant(np.pi / 2, dtype=dtype)],
-                                        prefer_static_shape(state.position))
-
-            # Evaluate three different point for curve fitting
-            v_l, v_n, v_r = expectation_value_function(
-                state.position - delta_shift), \
-                state.objective_value, \
-                expectation_value_function(state.position + delta_shift)
-
-            # Use the analytical solution to find the optimized position
-            delta_update = -np.pi / 2 - \
-                tf.math.atan2(2 * v_n - v_l - v_r, v_r - v_l)
-
-            delta_update_tensor = tf.scatter_nd(
-                [[state.solve_param_i]], [delta_update],
-                prefer_static_shape(state.position))
-
-            state.solve_param_i.assign_add(1)
-            state.position.assign(
-                tf.math.floormod(state.position + delta_update_tensor,
-                                 np.pi * 2))
-
-            state.objective_value_previous_iteration.assign(
-                state.objective_value)
-            state.objective_value.assign(
-                expectation_value_function(state.position))
-
-            return [state]
-
-        def _rotosolve_all_parameters_once(state):
-            """Iterate over all parameters and rotosolve each single
-
-            of them once.
-
-            Args:
-                state: A RotosolveOptimizerResults object stores the
-                       current state of the minimizer.
-
-            Returns:
-                states: A list which the first element is the new state
-            """
-
-            def _cond_internal(state_cond):
-                return state_cond.solve_param_i < \
-                       prefer_static_shape(state_cond.position)[0]
-
-            state.num_objective_evaluations.assign_add(1)
-
-            return tf.while_loop(
-                cond=_cond_internal,
-                body=_rotosolve_one_parameter_once,
-                loop_vars=[state],
-                parallel_iterations=1,
-            )
-
-        # The `state` here is a `RotosolveOptimizerResults` tuple with
-        # values for the current state of the algorithm computation.
-        def _cond(state):
-            """Continue if iterations remain and stopping condition
-            is not met."""
-            return (state.num_iterations < max_iterations) \
-                   and (not state.converged)
-
-        def _body(state):
-            """Main optimization loop."""
-
-            state.solve_param_i.assign(0)
-
-            _rotosolve_all_parameters_once(state)
-
-            state.num_iterations.assign_add(1)
-            state.converged.assign(
-                tf.abs(state.objective_value -
-                       state.objective_value_previous_iteration) <
-                state.tolerance)
-
-            return [state]
-
-        initial_state = _get_initial_state(initial_position, tolerance,
-                                           expectation_value_function)
-
-        initial_state.objective_value.assign(
-            expectation_value_function(initial_state.position))
-
-        return tf.while_loop(cond=_cond,
-                             body=_body,
-                             loop_vars=[initial_state],
-                             parallel_iterations=1)[0]
+# Copyright 2020 The TensorFlow Quantum Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""The rotosolve minimization algorithm"""
+import collections
+import numpy as np
+import tensorflow as tf
+
+
+def prefer_static_shape(x):
+    """Return static shape of tensor `x` if available,
+
+    else `tf.shape(x)`.
+
+    Args:
+        x: `tf.Tensor` (already converted).
+    Returns:
+        Numpy array (if static shape is obtainable), else `tf.Tensor`.
+    """
+    return prefer_static_value(tf.shape(x))
+
+
+def prefer_static_value(x):
+    """Return static value of tensor `x` if available, else `x`.
+
+    Args:
+        x: `tf.Tensor` (already converted).
+    Returns:
+        Numpy array (if static value is obtainable), else `tf.Tensor`.
+    """
+    static_x = tf.get_static_value(x)
+    if static_x is not None:
+        return static_x
+    return x
+
+
+class RotosolveOptimizerResults(tf.experimental.ExtensionType):
+    converged: tf.Tensor
+    # Scalar boolean tensor indicating whether the minimum
+    # was found within tolerance.
+    num_iterations: tf.Tensor
+    # The number of iterations of the rotosolve update.
+    num_objective_evaluations: tf.Tensor
+    # The total number of objective
+    # evaluations performed.
+    position: tf.Tensor
+    # A tensor containing the last argument value found
+    # during the search. If the search converged, then
+    # this value is the argmin of the objective function.
+    # A tensor containing the value of the objective from
+    # previous iteration
+    objective_value_previous_iteration: tf.Tensor
+    # Save the evaluated value of the objective function
+    # from the previous iteration
+    objective_value: tf.Tensor
+    # A tensor containing the value of the objective
+    # function at the `position`. If the search
+    # converged, then this is the (local) minimum of
+    # the objective function.
+    tolerance: tf.Tensor
+    # Define the stop criteria. Iteration will stop when the
+    # objective value difference between two iterations is
+    # smaller than tolerance
+    solve_param_i: tf.Tensor
+
+    # The parameter index where rotosolve is currently
+    # modifying. Reserved for internal use.
+
+    def to_dict(self):
+        return {
+            "converged":
+                self.converged,
+            "num_iterations":
+                self.num_iterations,
+            "num_objective_evaluations":
+                self.num_objective_evaluations,
+            "position":
+                self.position,
+            "objective_value":
+                self.objective_value,
+            "objective_value_previous_iteration":
+                self.objective_value_previous_iteration,
+            "tolerance":
+                self.tolerance,
+            "solve_param_i":
+                self.solve_param_i,
+        }
+
+
+def _get_initial_state(initial_position, tolerance, expectation_value_function):
+    """Create RotosolveOptimizerResults with initial state of search."""
+    init_args = {
+        "converged": tf.Variable(False),
+        "num_iterations": tf.Variable(0),
+        "num_objective_evaluations": tf.Variable(0),
+        "position": tf.Variable(initial_position),
+        "objective_value": expectation_value_function(initial_position),
+        "objective_value_previous_iteration": tf.Variable(0.),
+        "tolerance": tolerance,
+        "solve_param_i": tf.Variable(0),
+    }
+    return RotosolveOptimizerResults(**init_args)
+
+
+def minimize(expectation_value_function,
+             initial_position,
+             tolerance=1e-5,
+             max_iterations=50,
+             name=None):
+    """Applies the rotosolve algorithm.
+
+    The rotosolve algorithm can be used to minimize a linear combination
+
+    of quantum measurement expectation values. See the following paper:
+
+    [arXiv:1903.12166](https://arxiv.org/abs/1903.12166), Ken M. Nakanishi.
+    [arXiv:1905.09692](https://arxiv.org/abs/1905.09692), Mateusz Ostaszewski.
+
+    Usage:
+
+    Here is an example of optimize a function which consists summation of
+    a few sinusoids.
+
+    >>> n = 10  # Number of sinusoids
+    >>> coefficient = tf.random.uniform(shape=[n])
+    >>> min_value = -tf.math.reduce_sum(tf.abs(coefficient))
+    >>> func = lambda x:tf.math.reduce_sum(tf.sin(x) * coefficient)
+    >>> # Optimize the function with rotosolve, start with random parameters
+    >>> result = tfq.optimizers.rotosolve_minimize(func, np.random.random(n))
+    >>> result.converged
+    tf.Tensor(True, shape=(), dtype=bool)
+    >>> result.objective_value
+    tf.Tensor(-4.7045116, shape=(), dtype=float32)
+
+    Args:
+        expectation_value_function:  A Python callable that accepts
+            a point as a real `tf.Tensor` and returns a `tf.Tensor`s
+            of real dtype containing the value of the function.
+            The function to be minimized. The input is of shape `[n]`,
+            where `n` is the size of the trainable parameters.
+            The return value is a real `tf.Tensor` Scalar (matching shape
+            `[1]`).  This must be a linear combination of quantum
+            measurement expectation value, otherwise this algorithm cannot
+            work.
+        initial_position: Real `tf.Tensor` of shape `[n]`. The starting
+            point, or points when using batching dimensions, of the search
+            procedure. At these points the function value and the gradient
+            norm should be finite.
+        tolerance: Scalar `tf.Tensor` of real dtype. Specifies the tolerance
+            for the procedure. If the supremum norm between two iteration
+            vector is below this number, the algorithm is stopped.
+        name: (Optional) Python `str`. The name prefixed to the ops created
+            by this function. If not supplied, the default name 'minimize'
+            is used.
+
+    Returns:
+        optimizer_results: A RotosolveOptimizerResults object contains the
+            result of the optimization process.
+    """
+
+    with tf.name_scope(name or 'minimize'):
+        initial_position = tf.convert_to_tensor(initial_position,
+                                                name='initial_position',
+                                                dtype='float32')
+        dtype = initial_position.dtype.base_dtype
+        tolerance = tf.convert_to_tensor(tolerance,
+                                         dtype=dtype,
+                                         name='grad_tolerance')
+        max_iterations = tf.convert_to_tensor(max_iterations,
+                                              name='max_iterations')
+
+        def _rotosolve_one_parameter_once(state):
+            """Rotosolve a single parameter once.
+
+            Args:
+                state: A RotosolveOptimizerResults object stores the
+                       current state of the minimizer.
+
+            Returns:
+                states: A list which the first element is the new state
+            """
+            delta_shift = tf.scatter_nd([[state.solve_param_i]],
+                                        [tf.constant(np.pi / 2, dtype=dtype)],
+                                        prefer_static_shape(state.position))
+
+            # Evaluate three different point for curve fitting
+            v_l, v_n, v_r = expectation_value_function(
+                state.position - delta_shift), \
+                state.objective_value, \
+                expectation_value_function(state.position + delta_shift)
+
+            # Use the analytical solution to find the optimized position
+            delta_update = -np.pi / 2 - \
+                tf.math.atan2(2 * v_n - v_l - v_r, v_r - v_l)
+
+            delta_update_tensor = tf.scatter_nd(
+                [[state.solve_param_i]], [delta_update],
+                prefer_static_shape(state.position))
+
+            new_position = (tf.math.floormod(
+                state.position + delta_update_tensor, np.pi * 2))
+            next_state_params = state.to_dict()
+            next_state_params.update({
+                "solve_param_i": state.solve_param_i + 1,
+                "position": new_position,
+                "objective_value_previous_iteration": state.objective_value,
+                "objective_value": (expectation_value_function(new_position)),
+            })
+            return [RotosolveOptimizerResults(**next_state_params)]
+
+        def _rotosolve_all_parameters_once(state):
+            """Iterate over all parameters and rotosolve each single
+
+            of them once.
+
+            Args:
+                state: A RotosolveOptimizerResults object stores the
+                       current state of the minimizer.
+
+            Returns:
+                states: A list which the first element is the new state
+            """
+
+            def _cond_internal(state_cond):
+                return state_cond.solve_param_i < \
+                       prefer_static_shape(state_cond.position)[0]
+
+            next_state_params = state.to_dict()
+            next_state_params.update({
+                "num_objective_evaluations":
+                    state.num_objective_evaluations + 1,
+            })
+
+            return tf.while_loop(
+                cond=_cond_internal,
+                body=_rotosolve_one_parameter_once,
+                loop_vars=[RotosolveOptimizerResults(**next_state_params)],
+                parallel_iterations=1,
+            )
+
+        # The `state` here is a `RotosolveOptimizerResults` tuple with
+        # values for the current state of the algorithm computation.
+        def _cond(state):
+            """Continue if iterations remain and stopping condition
+            is not met."""
+            return (state.num_iterations < max_iterations) \
+                   and (not state.converged)
+
+        def _body(state):
+            """Main optimization loop."""
+            pre_state_params = state.to_dict()
+            pre_state_params.update({"solve_param_i": 0})
+            pre_state = RotosolveOptimizerResults(**pre_state_params)
+            post_state = _rotosolve_all_parameters_once(pre_state)[0]
+            next_state_params = post_state.to_dict()
+            next_state_params.update({
+                "converged":
+                    (tf.abs(post_state.objective_value -
+                            post_state.objective_value_previous_iteration) <
+                     post_state.tolerance),
+                "num_iterations": post_state.num_iterations + 1,
+            })
+            return [RotosolveOptimizerResults(**next_state_params)]
+
+        initial_state = _get_initial_state(initial_position, tolerance,
+                                           expectation_value_function)
+
+        return tf.while_loop(cond=_cond,
+                             body=_body,
+                             loop_vars=[initial_state],
+                             parallel_iterations=1)[0]
diff --git a/tensorflow_quantum/python/optimizers/spsa_minimizer.py b/tensorflow_quantum/python/optimizers/spsa_minimizer.py
index ecabda275..c791eed0e 100644
--- a/tensorflow_quantum/python/optimizers/spsa_minimizer.py
+++ b/tensorflow_quantum/python/optimizers/spsa_minimizer.py
@@ -45,50 +45,78 @@ def prefer_static_value(x):
     return x
 
 
-SPSAOptimizerResults = collections.namedtuple(
-    'SPSAOptimizerResults',
-    [
-        'converged',
-        # Scalar boolean tensor indicating whether the minimum
-        # was found within tolerance.
-        'num_iterations',
-        # The number of iterations of the SPSA update.
-        'num_objective_evaluations',
-        # The total number of objective
-        # evaluations performed.
-        'position',
-        # A tensor containing the last argument value found
-        # during the search. If the search converged, then
-        # this value is the argmin of the objective function.
-        # A tensor containing the value of the objective from
-        # previous iteration
-        'objective_value_previous_iteration',
-        # Save the evaluated value of the objective function
-        # from the previous iteration
-        'objective_value',
-        # A tensor containing the value of the objective
-        # function at the `position`. If the search
-        # converged, then this is the (local) minimum of
-        # the objective function.
-        'tolerance',
-        # Define the stop criteria. Iteration will stop when the
-        # objective value difference between two iterations is
-        # smaller than tolerance
-        'lr',
-        # Specifies the learning rate
-        'alpha',
-        # Specifies scaling of the learning rate
-        'perturb',
-        # Specifies the size of the perturbations
-        'gamma',
-        # Specifies scaling of the size of the perturbations
-        'blocking',
-        # If true, then the optimizer will only accept updates that improve
-        # the objective function.
-        'allowed_increase'
-        # Specifies maximum allowable increase in objective function
-        # (only applies if blocking is true).
-    ])
+class SPSAOptimizerResults(tf.experimental.ExtensionType):
+    converged: tf.Tensor
+    # Scalar boolean tensor indicating whether the minimum
+    # was found within tolerance.
+    num_iterations: tf.Tensor
+    # The number of iterations of the SPSA update.
+    num_objective_evaluations: tf.Tensor
+    # The total number of objective
+    # evaluations performed.
+    position: tf.Tensor
+    # A tensor containing the last argument value found
+    # during the search. If the search converged, then
+    # this value is the argmin of the objective function.
+    # A tensor containing the value of the objective from
+    # previous iteration
+    objective_value_previous_iteration: tf.Tensor
+    # Save the evaluated value of the objective function
+    # from the previous iteration
+    objective_value: tf.Tensor
+    # A tensor containing the value of the objective
+    # function at the `position`. If the search
+    # converged, then this is the (local) minimum of
+    # the objective function.
+    tolerance: tf.Tensor
+    # Define the stop criteria. Iteration will stop when the
+    # objective value difference between two iterations is
+    # smaller than tolerance
+    lr: tf.Tensor
+    # Specifies the learning rate
+    alpha: tf.Tensor
+    # Specifies scaling of the learning rate
+    perturb: tf.Tensor
+    # Specifies the size of the perturbations
+    gamma: tf.Tensor
+    # Specifies scaling of the size of the perturbations
+    blocking: tf.Tensor
+    # If true, then the optimizer will only accept updates that improve
+    # the objective function.
+    allowed_increase: tf.Tensor
+
+    # Specifies maximum allowable increase in objective function
+    # (only applies if blocking is true).
+
+    def to_dict(self):
+        return {
+            "converged":
+                self.converged,
+            "num_iterations":
+                self.num_iterations,
+            "num_objective_evaluations":
+                self.num_objective_evaluations,
+            "position":
+                self.position,
+            "objective_value":
+                self.objective_value,
+            "objective_value_previous_iteration":
+                self.objective_value_previous_iteration,
+            "tolerance":
+                self.tolerance,
+            "lr":
+                self.lr,
+            "alpha":
+                self.alpha,
+            "perturb":
+                self.perturb,
+            "gamma":
+                self.gamma,
+            "blocking":
+                self.blocking,
+            "allowed_increase":
+                self.allowed_increase,
+        }
 
 
 def _get_initial_state(initial_position, tolerance, expectation_value_function,
@@ -99,7 +127,8 @@ def _get_initial_state(initial_position, tolerance, expectation_value_function,
         "num_iterations": tf.Variable(0),
         "num_objective_evaluations": tf.Variable(0),
         "position": tf.Variable(initial_position),
-        "objective_value": tf.Variable(0.),
+        "objective_value":
+            (tf.cast(expectation_value_function(initial_position), tf.float32)),
         "objective_value_previous_iteration": tf.Variable(np.inf),
         "tolerance": tolerance,
         "lr": tf.Variable(lr),
@@ -107,7 +136,7 @@ def _get_initial_state(initial_position, tolerance, expectation_value_function,
         "perturb": tf.Variable(perturb),
         "gamma": tf.Variable(gamma),
         "blocking": tf.Variable(blocking),
-        "allowed_increase": tf.Variable(allowed_increase)
+        "allowed_increase": tf.Variable(allowed_increase),
     }
     return SPSAOptimizerResults(**init_args)
 
@@ -225,18 +254,26 @@ def _spsa_once(state):
 
             gradient_estimate = (v_p - v_m) / (2 * state.perturb) * delta_shift
             update = state.lr * gradient_estimate
-
-            state.num_objective_evaluations.assign_add(2)
-
-            current_obj = expectation_value_function(state.position - update)
+            next_state_params = state.to_dict()
+            next_state_params.update({
+                "num_objective_evaluations":
+                    state.num_objective_evaluations + 2,
+            })
+
+            current_obj = tf.cast(expectation_value_function(state.position -
+                                                             update),
+                                  dtype=tf.float32)
             if state.objective_value_previous_iteration + \
                 state.allowed_increase >= current_obj or not state.blocking:
-                state.position.assign(state.position - update)
-                state.objective_value_previous_iteration.assign(
-                    state.objective_value)
-                state.objective_value.assign(current_obj)
+                next_state_params.update({
+                    "position": state.position - update,
+                    "objective_value_previous_iteration": state.objective_value,
+                    "objective_value": current_obj
+                })
+
+                return [SPSAOptimizerResults(**next_state_params)]
 
-            return [state]
+            return [SPSAOptimizerResults(**next_state_params)]
 
         # The `state` here is a `SPSAOptimizerResults` tuple with
         # values for the current state of the algorithm computation.
@@ -254,26 +291,30 @@ def _body(state):
             new_perturb = perturb_init / (tf.cast(state.num_iterations + 1,
                                                   tf.float32)**state.gamma)
 
-            state.lr.assign(new_lr)
-            state.perturb.assign(new_perturb)
-
-            _spsa_once(state)
-            state.num_iterations.assign_add(1)
-            state.converged.assign(
-                tf.abs(state.objective_value -
-                       state.objective_value_previous_iteration) <
-                state.tolerance)
-            return [state]
+            pre_state_params = state.to_dict()
+            pre_state_params.update({
+                "lr": new_lr,
+                "perturb": new_perturb,
+            })
+
+            post_state = _spsa_once(SPSAOptimizerResults(**pre_state_params))[0]
+            post_state_params = post_state.to_dict()
+            tf.print("asdf", state.objective_value.dtype,
+                     state.objective_value_previous_iteration.dtype)
+            post_state_params.update({
+                "num_iterations":
+                    post_state.num_iterations + 1,
+                "converged": (tf.abs(state.objective_value -
+                                     state.objective_value_previous_iteration) <
+                              state.tolerance),
+            })
+            return [SPSAOptimizerResults(**post_state_params)]
 
         initial_state = _get_initial_state(initial_position, tolerance,
                                            expectation_value_function, lr,
                                            alpha, perturb, gamma, blocking,
                                            allowed_increase)
 
-        initial_state.objective_value.assign(
-            tf.cast(expectation_value_function(initial_state.position),
-                    tf.float32))
-
         return tf.while_loop(cond=_cond,
                              body=_body,
                              loop_vars=[initial_state],

From 39a811d1a2ba8e854f800ba23157a7cc017a8cf4 Mon Sep 17 00:00:00 2001
From: Jae Yoo <jaeyoo@google.com>
Date: Wed, 15 Mar 2023 15:05:34 -0700
Subject: [PATCH 06/13] Format optimizers

---
 .../python/optimizers/rotosolve_minimizer.py  | 42 ++++-----
 .../python/optimizers/spsa_minimizer.py       | 88 +++++++++----------
 2 files changed, 57 insertions(+), 73 deletions(-)

diff --git a/tensorflow_quantum/python/optimizers/rotosolve_minimizer.py b/tensorflow_quantum/python/optimizers/rotosolve_minimizer.py
index cac3c89a4..5bf23f6a0 100755
--- a/tensorflow_quantum/python/optimizers/rotosolve_minimizer.py
+++ b/tensorflow_quantum/python/optimizers/rotosolve_minimizer.py
@@ -12,8 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""The rotosolve minimization algorithm"""
-import collections
+"""The rotosolve minimization algorithm."""
 import numpy as np
 import tensorflow as tf
 
@@ -46,6 +45,7 @@ def prefer_static_value(x):
 
 
 class RotosolveOptimizerResults(tf.experimental.ExtensionType):
+    """ExtentionType of Rotosolve Optimizer tf.while_loop() inner state."""
     converged: tf.Tensor
     # Scalar boolean tensor indicating whether the minimum
     # was found within tolerance.
@@ -60,7 +60,7 @@ class RotosolveOptimizerResults(tf.experimental.ExtensionType):
     # this value is the argmin of the objective function.
     # A tensor containing the value of the objective from
     # previous iteration
-    objective_value_previous_iteration: tf.Tensor
+    objective_value_prev: tf.Tensor
     # Save the evaluated value of the objective function
     # from the previous iteration
     objective_value: tf.Tensor
@@ -78,23 +78,16 @@ class RotosolveOptimizerResults(tf.experimental.ExtensionType):
     # modifying. Reserved for internal use.
 
     def to_dict(self):
+        """Transforms immutable data to mutable dictionary."""
         return {
-            "converged":
-                self.converged,
-            "num_iterations":
-                self.num_iterations,
-            "num_objective_evaluations":
-                self.num_objective_evaluations,
-            "position":
-                self.position,
-            "objective_value":
-                self.objective_value,
-            "objective_value_previous_iteration":
-                self.objective_value_previous_iteration,
-            "tolerance":
-                self.tolerance,
-            "solve_param_i":
-                self.solve_param_i,
+            "converged": self.converged,
+            "num_iterations": self.num_iterations,
+            "num_objective_evaluations": self.num_objective_evaluations,
+            "position": self.position,
+            "objective_value": self.objective_value,
+            "objective_value_prev": self.objective_value_prev,
+            "tolerance": self.tolerance,
+            "solve_param_i": self.solve_param_i,
         }
 
 
@@ -106,7 +99,7 @@ def _get_initial_state(initial_position, tolerance, expectation_value_function):
         "num_objective_evaluations": tf.Variable(0),
         "position": tf.Variable(initial_position),
         "objective_value": expectation_value_function(initial_position),
-        "objective_value_previous_iteration": tf.Variable(0.),
+        "objective_value_prev": tf.Variable(0.),
         "tolerance": tolerance,
         "solve_param_i": tf.Variable(0),
     }
@@ -214,7 +207,7 @@ def _rotosolve_one_parameter_once(state):
             next_state_params.update({
                 "solve_param_i": state.solve_param_i + 1,
                 "position": new_position,
-                "objective_value_previous_iteration": state.objective_value,
+                "objective_value_prev": state.objective_value,
                 "objective_value": (expectation_value_function(new_position)),
             })
             return [RotosolveOptimizerResults(**next_state_params)]
@@ -265,10 +258,9 @@ def _body(state):
             post_state = _rotosolve_all_parameters_once(pre_state)[0]
             next_state_params = post_state.to_dict()
             next_state_params.update({
-                "converged":
-                    (tf.abs(post_state.objective_value -
-                            post_state.objective_value_previous_iteration) <
-                     post_state.tolerance),
+                "converged": (tf.abs(post_state.objective_value -
+                                     post_state.objective_value_prev) <
+                              post_state.tolerance),
                 "num_iterations": post_state.num_iterations + 1,
             })
             return [RotosolveOptimizerResults(**next_state_params)]
diff --git a/tensorflow_quantum/python/optimizers/spsa_minimizer.py b/tensorflow_quantum/python/optimizers/spsa_minimizer.py
index c791eed0e..28674ca20 100644
--- a/tensorflow_quantum/python/optimizers/spsa_minimizer.py
+++ b/tensorflow_quantum/python/optimizers/spsa_minimizer.py
@@ -12,8 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""The SPSA minimization algorithm"""
-import collections
+"""The SPSA minimization algorithm."""
 import tensorflow as tf
 import numpy as np
 
@@ -46,6 +45,7 @@ def prefer_static_value(x):
 
 
 class SPSAOptimizerResults(tf.experimental.ExtensionType):
+    """ExtentionType of SPSA Optimizer tf.while_loop() inner state."""
     converged: tf.Tensor
     # Scalar boolean tensor indicating whether the minimum
     # was found within tolerance.
@@ -60,7 +60,7 @@ class SPSAOptimizerResults(tf.experimental.ExtensionType):
     # this value is the argmin of the objective function.
     # A tensor containing the value of the objective from
     # previous iteration
-    objective_value_previous_iteration: tf.Tensor
+    objective_value_prev: tf.Tensor
     # Save the evaluated value of the objective function
     # from the previous iteration
     objective_value: tf.Tensor
@@ -72,7 +72,7 @@ class SPSAOptimizerResults(tf.experimental.ExtensionType):
     # Define the stop criteria. Iteration will stop when the
     # objective value difference between two iterations is
     # smaller than tolerance
-    lr: tf.Tensor
+    learning_rate: tf.Tensor
     # Specifies the learning rate
     alpha: tf.Tensor
     # Specifies scaling of the learning rate
@@ -89,38 +89,27 @@ class SPSAOptimizerResults(tf.experimental.ExtensionType):
     # (only applies if blocking is true).
 
     def to_dict(self):
+        """Transforms immutable data to mutable dictionary."""
         return {
-            "converged":
-                self.converged,
-            "num_iterations":
-                self.num_iterations,
-            "num_objective_evaluations":
-                self.num_objective_evaluations,
-            "position":
-                self.position,
-            "objective_value":
-                self.objective_value,
-            "objective_value_previous_iteration":
-                self.objective_value_previous_iteration,
-            "tolerance":
-                self.tolerance,
-            "lr":
-                self.lr,
-            "alpha":
-                self.alpha,
-            "perturb":
-                self.perturb,
-            "gamma":
-                self.gamma,
-            "blocking":
-                self.blocking,
-            "allowed_increase":
-                self.allowed_increase,
+            "converged": self.converged,
+            "num_iterations": self.num_iterations,
+            "num_objective_evaluations": self.num_objective_evaluations,
+            "position": self.position,
+            "objective_value": self.objective_value,
+            "objective_value_prev": self.objective_value_prev,
+            "tolerance": self.tolerance,
+            "learning_rate": self.learning_rate,
+            "alpha": self.alpha,
+            "perturb": self.perturb,
+            "gamma": self.gamma,
+            "blocking": self.blocking,
+            "allowed_increase": self.allowed_increase,
         }
 
 
 def _get_initial_state(initial_position, tolerance, expectation_value_function,
-                       lr, alpha, perturb, gamma, blocking, allowed_increase):
+                       learning_rate, alpha, perturb, gamma, blocking,
+                       allowed_increase):
     """Create SPSAOptimizerResults with initial state of search."""
     init_args = {
         "converged": tf.Variable(False),
@@ -129,9 +118,9 @@ def _get_initial_state(initial_position, tolerance, expectation_value_function,
         "position": tf.Variable(initial_position),
         "objective_value":
             (tf.cast(expectation_value_function(initial_position), tf.float32)),
-        "objective_value_previous_iteration": tf.Variable(np.inf),
+        "objective_value_prev": tf.Variable(np.inf),
         "tolerance": tolerance,
-        "lr": tf.Variable(lr),
+        "learning_rate": tf.Variable(learning_rate),
         "alpha": tf.Variable(alpha),
         "perturb": tf.Variable(perturb),
         "gamma": tf.Variable(gamma),
@@ -146,7 +135,7 @@ def minimize(expectation_value_function,
              tolerance=1e-5,
              max_iterations=200,
              alpha=0.602,
-             lr=1.0,
+             learning_rate=1.0,
              perturb=1.0,
              gamma=0.101,
              blocking=False,
@@ -188,7 +177,8 @@ def minimize(expectation_value_function,
         tolerance: Scalar `tf.Tensor` of real dtype. Specifies the tolerance
             for the procedure. If the supremum norm between two iteration
             vector is below this number, the algorithm is stopped.
-        lr: Scalar `tf.Tensor` of real dtype. Specifies the learning rate
+        learning_rate: Scalar `tf.Tensor` of real dtype.
+            Specifies the learning rate.
         alpha: Scalar `tf.Tensor` of real dtype. Specifies scaling of the
             learning rate.
         perturb: Scalar `tf.Tensor` of real dtype. Specifies the size of the
@@ -227,7 +217,9 @@ def minimize(expectation_value_function,
         max_iterations = tf.convert_to_tensor(max_iterations,
                                               name='max_iterations')
 
-        lr_init = tf.convert_to_tensor(lr, name='initial_a', dtype='float32')
+        learning_rate_init = tf.convert_to_tensor(learning_rate,
+                                                  name='initial_a',
+                                                  dtype='float32')
         perturb_init = tf.convert_to_tensor(perturb,
                                             name='initial_c',
                                             dtype='float32')
@@ -253,7 +245,7 @@ def _spsa_once(state):
                                              state.perturb * delta_shift)
 
             gradient_estimate = (v_p - v_m) / (2 * state.perturb) * delta_shift
-            update = state.lr * gradient_estimate
+            update = state.learning_rate * gradient_estimate
             next_state_params = state.to_dict()
             next_state_params.update({
                 "num_objective_evaluations":
@@ -263,11 +255,11 @@ def _spsa_once(state):
             current_obj = tf.cast(expectation_value_function(state.position -
                                                              update),
                                   dtype=tf.float32)
-            if state.objective_value_previous_iteration + \
+            if state.objective_value_prev + \
                 state.allowed_increase >= current_obj or not state.blocking:
                 next_state_params.update({
                     "position": state.position - update,
-                    "objective_value_previous_iteration": state.objective_value,
+                    "objective_value_prev": state.objective_value,
                     "objective_value": current_obj
                 })
 
@@ -285,7 +277,7 @@ def _cond(state):
 
         def _body(state):
             """Main optimization loop."""
-            new_lr = lr_init / (
+            new_learning_rate = learning_rate_init / (
                 (tf.cast(state.num_iterations + 1, tf.float32) +
                  0.01 * tf.cast(max_iterations, tf.float32))**state.alpha)
             new_perturb = perturb_init / (tf.cast(state.num_iterations + 1,
@@ -293,27 +285,27 @@ def _body(state):
 
             pre_state_params = state.to_dict()
             pre_state_params.update({
-                "lr": new_lr,
+                "learning_rate": new_learning_rate,
                 "perturb": new_perturb,
             })
 
             post_state = _spsa_once(SPSAOptimizerResults(**pre_state_params))[0]
             post_state_params = post_state.to_dict()
             tf.print("asdf", state.objective_value.dtype,
-                     state.objective_value_previous_iteration.dtype)
+                     state.objective_value_prev.dtype)
             post_state_params.update({
                 "num_iterations":
                     post_state.num_iterations + 1,
-                "converged": (tf.abs(state.objective_value -
-                                     state.objective_value_previous_iteration) <
-                              state.tolerance),
+                "converged":
+                    (tf.abs(state.objective_value - state.objective_value_prev)
+                     < state.tolerance),
             })
             return [SPSAOptimizerResults(**post_state_params)]
 
         initial_state = _get_initial_state(initial_position, tolerance,
-                                           expectation_value_function, lr,
-                                           alpha, perturb, gamma, blocking,
-                                           allowed_increase)
+                                           expectation_value_function,
+                                           learning_rate, alpha, perturb, gamma,
+                                           blocking, allowed_increase)
 
         return tf.while_loop(cond=_cond,
                              body=_body,

From 9b94ebf2e4ff4cbabacf03bd52eebab4327409f0 Mon Sep 17 00:00:00 2001
From: Jae Yoo <jaeyoo@google.com>
Date: Wed, 15 Mar 2023 15:51:34 -0700
Subject: [PATCH 07/13] Upgrade the latest gym for notebook tutorial.

---
 scripts/ci_validate_tutorials.sh | 4 ++--
 scripts/test_tutorials.py        | 6 ++----
 2 files changed, 4 insertions(+), 6 deletions(-)

diff --git a/scripts/ci_validate_tutorials.sh b/scripts/ci_validate_tutorials.sh
index 74c9189a1..8dcd5af27 100755
--- a/scripts/ci_validate_tutorials.sh
+++ b/scripts/ci_validate_tutorials.sh
@@ -15,11 +15,11 @@
 # ==============================================================================
 
 # Run the tutorials using the installed pip package
-pip install jupyter nbconvert==6.4.3 jupyter-client==6.1.12 ipython==7.22.0
+pip install jupyter nbclient==0.6.5 jupyter-client==6.1.12 ipython==7.22.0
 # Workaround for ipykernel - see https://github.com/ipython/ipykernel/issues/422
 pip install ipykernel==5.1.1
 # OpenAI Gym pip package needed for the quantum reinforcement learning tutorial
-pip install gym==0.18.0
+pip install gym==0.24.1
 # tf_docs pip package needed for noise tutorial.
 pip install -q git+https://github.com/tensorflow/docs
 # Leave the quantum directory, otherwise errors may occur
diff --git a/scripts/test_tutorials.py b/scripts/test_tutorials.py
index 13df27aef..593e425e5 100644
--- a/scripts/test_tutorials.py
+++ b/scripts/test_tutorials.py
@@ -18,7 +18,7 @@
 
 from absl.testing import parameterized
 import nbformat
-import nbconvert
+import nbclient
 import tensorflow as tf
 
 # Must be run from the directory containing `quantum` repo.
@@ -46,9 +46,7 @@ def test_notebook(self, path):
                 src = re.sub('n_epochs ?= ?.*', 'n_epochs = 2', src)
                 cell['source'] = src
 
-        _ = nbconvert.preprocessors.execute.executenb(nb,
-                                                      timeout=900,
-                                                      kernel_name="python3")
+        _ =  nbclient.execute(nb, timeout=900, kernel_name="python3")
 
 
 if __name__ == "__main__":

From c5df061088fc7d69fde7f8f695e0a43fd2a32213 Mon Sep 17 00:00:00 2001
From: Jae Yoo <jaeyoo@google.com>
Date: Wed, 15 Mar 2023 16:08:12 -0700
Subject: [PATCH 08/13] Fix tutorial error

---
 docs/tutorials/gradients.ipynb | 57 ++++++++++++++++++++--------------
 scripts/test_tutorials.py      |  2 +-
 2 files changed, 34 insertions(+), 25 deletions(-)

diff --git a/docs/tutorials/gradients.ipynb b/docs/tutorials/gradients.ipynb
index 2ade263a6..809d03c42 100644
--- a/docs/tutorials/gradients.ipynb
+++ b/docs/tutorials/gradients.ipynb
@@ -12,7 +12,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "cellView": "form",
         "colab": {},
@@ -91,7 +91,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
@@ -99,7 +99,7 @@
       },
       "outputs": [],
       "source": [
-        "!pip install tensorflow==2.7.0"
+        "!pip install tensorflow==2.11.0"
       ]
     },
     {
@@ -114,7 +114,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
@@ -122,12 +122,12 @@
       },
       "outputs": [],
       "source": [
-        "!pip install tensorflow-quantum==0.7.2"
+        "!pip install tensorflow-quantum==0.7.3"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
@@ -152,7 +152,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
@@ -187,7 +187,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
@@ -212,7 +212,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
@@ -236,7 +236,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
@@ -269,7 +269,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
@@ -302,7 +302,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
@@ -331,7 +331,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
@@ -361,7 +361,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
@@ -370,7 +370,7 @@
       "outputs": [],
       "source": [
         "# Make input_points = [batch_size, 1] array.\n",
-        "input_points = np.linspace(0, 5, 200)[:, np.newaxis].astype(float)\n",
+        "input_points = np.linspace(0, 5, 200)[:, np.newaxis].astype(np.float32)\n",
         "exact_outputs = expectation_calculation(my_circuit,\n",
         "                                        operators=pauli_x,\n",
         "                                        symbol_names=['alpha'],\n",
@@ -390,7 +390,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
@@ -439,7 +439,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
@@ -494,7 +494,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
@@ -518,7 +518,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
@@ -548,7 +548,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
@@ -577,7 +577,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
@@ -636,7 +636,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
@@ -694,7 +694,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
@@ -750,7 +750,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
@@ -812,6 +812,15 @@
       "display_name": "Python 3",
       "language": "python",
       "name": "python3"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.10.9 (main, Dec  7 2022, 13:47:07) [GCC 12.2.0]"
+    },
+    "vscode": {
+      "interpreter": {
+        "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
+      }
     }
   },
   "nbformat": 4,
diff --git a/scripts/test_tutorials.py b/scripts/test_tutorials.py
index 593e425e5..1650caf15 100644
--- a/scripts/test_tutorials.py
+++ b/scripts/test_tutorials.py
@@ -46,7 +46,7 @@ def test_notebook(self, path):
                 src = re.sub('n_epochs ?= ?.*', 'n_epochs = 2', src)
                 cell['source'] = src
 
-        _ =  nbclient.execute(nb, timeout=900, kernel_name="python3")
+        _ = nbclient.execute(nb, timeout=900, kernel_name="python3")
 
 
 if __name__ == "__main__":

From c185891066ce453dcac56fc19d172bd71150a031 Mon Sep 17 00:00:00 2001
From: Jae Yoo <jaeyoo@google.com>
Date: Wed, 15 Mar 2023 17:24:24 -0700
Subject: [PATCH 09/13] Fix tutorial errors

---
 docs/tutorials/barren_plateaus.ipynb          |  31 +-
 docs/tutorials/gradients.ipynb                |   4 +-
 docs/tutorials/hello_many_worlds.ipynb        | 267 +++++++++++++-----
 docs/tutorials/mnist.ipynb                    |  17 +-
 docs/tutorials/noise.ipynb                    | 234 +++++++--------
 docs/tutorials/qcnn.ipynb                     | 203 +++++++++----
 docs/tutorials/quantum_data.ipynb             |  42 ++-
 .../quantum_reinforcement_learning.ipynb      |  31 +-
 8 files changed, 522 insertions(+), 307 deletions(-)

diff --git a/docs/tutorials/barren_plateaus.ipynb b/docs/tutorials/barren_plateaus.ipynb
index b35e4f76c..3c9176eaa 100644
--- a/docs/tutorials/barren_plateaus.ipynb
+++ b/docs/tutorials/barren_plateaus.ipynb
@@ -12,7 +12,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "cellView": "form",
         "colab": {},
@@ -89,7 +89,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
@@ -112,7 +112,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
@@ -125,7 +125,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
@@ -150,7 +150,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
@@ -202,7 +202,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
@@ -278,7 +278,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
@@ -297,7 +297,7 @@
         "    # Prep the inputs as tensors\n",
         "    circuit_tensor = tfq.convert_to_tensor(circuits)\n",
         "    values_tensor = tf.convert_to_tensor(\n",
-        "        np.random.uniform(0, 2 * np.pi, (n_circuits, 1)).astype(float))\n",
+        "        np.random.uniform(0, 2 * np.pi, (n_circuits, 1)).astype(np.float32))\n",
         "\n",
         "    # Use TensorFlow GradientTape to track gradients.\n",
         "    with tf.GradientTape() as g:\n",
@@ -327,7 +327,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
@@ -399,7 +399,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
@@ -455,7 +455,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
@@ -511,6 +511,15 @@
       "display_name": "Python 3",
       "language": "python",
       "name": "python3"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.10.9 (main, Dec  7 2022, 13:47:07) [GCC 12.2.0]"
+    },
+    "vscode": {
+      "interpreter": {
+        "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
+      }
     }
   },
   "nbformat": 4,
diff --git a/docs/tutorials/gradients.ipynb b/docs/tutorials/gradients.ipynb
index 809d03c42..072718bcf 100644
--- a/docs/tutorials/gradients.ipynb
+++ b/docs/tutorials/gradients.ipynb
@@ -99,7 +99,7 @@
       },
       "outputs": [],
       "source": [
-        "!pip install tensorflow==2.11.0"
+        "!pip install tensorflow==2.7.0"
       ]
     },
     {
@@ -122,7 +122,7 @@
       },
       "outputs": [],
       "source": [
-        "!pip install tensorflow-quantum==0.7.3"
+        "!pip install tensorflow-quantum==0.7.2"
       ]
     },
     {
diff --git a/docs/tutorials/hello_many_worlds.ipynb b/docs/tutorials/hello_many_worlds.ipynb
index 75ff512a9..229136219 100644
--- a/docs/tutorials/hello_many_worlds.ipynb
+++ b/docs/tutorials/hello_many_worlds.ipynb
@@ -12,12 +12,15 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "cellView": "form",
         "colab": {},
         "colab_type": "code",
-        "id": "iiQkM5ZgQ8r2"
+        "id": "iiQkM5ZgQ8r2",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -89,11 +92,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "TorxE5tnkvb2"
+        "id": "TorxE5tnkvb2",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -112,11 +118,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "saFHsRDpkvkH"
+        "id": "saFHsRDpkvkH",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -125,11 +134,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "4Ql5PW-ACO0J"
+        "id": "4Ql5PW-ACO0J",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -150,11 +162,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "enZ300Bflq80"
+        "id": "enZ300Bflq80",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -197,11 +212,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "2yQdmhQLCrzQ"
+        "id": "2yQdmhQLCrzQ",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -220,11 +238,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "Ps-pd2mndXs7"
+        "id": "Ps-pd2mndXs7",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -251,11 +272,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "VMq7EayNRyQb"
+        "id": "VMq7EayNRyQb",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -277,12 +301,15 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
         "id": "hrSnOCi3ehr_",
-        "scrolled": true
+        "scrolled": true,
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -295,11 +322,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "OZ0lWFXv6pII"
+        "id": "OZ0lWFXv6pII",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -322,12 +352,15 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
         "id": "1gLQjA02mIyy",
-        "scrolled": true
+        "scrolled": true,
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -350,11 +383,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "aX_vEmCKmpQS"
+        "id": "aX_vEmCKmpQS",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -381,11 +417,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "1fsVZhF5lIXp"
+        "id": "1fsVZhF5lIXp",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -404,11 +443,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "RsfF53UCJtr9"
+        "id": "RsfF53UCJtr9",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -439,11 +481,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "kGZVdcZ6y9lC"
+        "id": "kGZVdcZ6y9lC",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -497,11 +542,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "N-j7SCl-51-q"
+        "id": "N-j7SCl-51-q",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -532,11 +580,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "1v4CK2jD6pIj"
+        "id": "1v4CK2jD6pIj",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -561,11 +612,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "kZbYRTe16pIm"
+        "id": "kZbYRTe16pIm",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -598,11 +652,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "UfHF8NNE6pIr"
+        "id": "UfHF8NNE6pIr",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -630,11 +687,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "Zvt2YGmZ6pIu"
+        "id": "Zvt2YGmZ6pIu",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -659,11 +719,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "Xs6EMhah6pIz"
+        "id": "Xs6EMhah6pIz",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -687,11 +750,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "ERXNPe4F6pI4"
+        "id": "ERXNPe4F6pI4",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -730,19 +796,22 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "ciMIJAuH6pJA"
+        "id": "ciMIJAuH6pJA",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
         "# The command input values to the classical NN.\n",
-        "commands = np.array([[0], [1]], dtype=float)\n",
+        "commands = np.array([[0], [1]], dtype=np.float32)\n",
         "\n",
         "# The desired Z expectation value at output of quantum circuit.\n",
-        "expected_outputs = np.array([[1], [-1]], dtype=float)"
+        "expected_outputs = np.array([[1], [-1]], dtype=np.float32)"
       ]
     },
     {
@@ -770,11 +839,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "_VYfzHffWo7n"
+        "id": "_VYfzHffWo7n",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -801,11 +873,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "6nk2Yr3e6pJJ"
+        "id": "6nk2Yr3e6pJJ",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -834,11 +909,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "Lwphqvs96pJO"
+        "id": "Lwphqvs96pJO",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -857,11 +935,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "dtPYqbNi8zeZ"
+        "id": "dtPYqbNi8zeZ",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -876,11 +957,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "azE-qV0OaC1o"
+        "id": "azE-qV0OaC1o",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -914,11 +998,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "RoIlb7r7j5SY"
+        "id": "RoIlb7r7j5SY",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -957,11 +1044,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "aYskLTacs8Ku"
+        "id": "aYskLTacs8Ku",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -998,11 +1088,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "hta0G3Nc6pJY"
+        "id": "hta0G3Nc6pJY",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -1031,11 +1124,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "n_aTG4g3-y0F"
+        "id": "n_aTG4g3-y0F",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -1058,11 +1154,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "IMHjiKit6pJg"
+        "id": "IMHjiKit6pJg",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -1096,11 +1195,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "4gw_L3JG0_G0"
+        "id": "4gw_L3JG0_G0",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -1108,10 +1210,10 @@
         "operator_data = tfq.convert_to_tensor([[cirq.X(qubit)], [cirq.Z(qubit)]])\n",
         "\n",
         "# The command input values to the classical NN.\n",
-        "commands = np.array([[0], [1]], dtype=float)\n",
+        "commands = np.array([[0], [1]], dtype=np.float32)\n",
         "\n",
         "# The desired expectation value at output of quantum circuit.\n",
-        "expected_outputs = np.array([[1], [-1]], dtype=float)"
+        "expected_outputs = np.array([[1], [-1]], dtype=np.float32)"
       ]
     },
     {
@@ -1128,11 +1230,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "nFuGA73MAA4p"
+        "id": "nFuGA73MAA4p",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -1150,11 +1255,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "Cf_G-GdturLL"
+        "id": "Cf_G-GdturLL",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -1187,11 +1295,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "uXmH0TQ76pJt"
+        "id": "uXmH0TQ76pJt",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
diff --git a/docs/tutorials/mnist.ipynb b/docs/tutorials/mnist.ipynb
index baed1eac9..91405ed26 100644
--- a/docs/tutorials/mnist.ipynb
+++ b/docs/tutorials/mnist.ipynb
@@ -112,7 +112,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
@@ -125,7 +125,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
@@ -480,8 +480,8 @@
       "source": [
         "THRESHOLD = 0.5\n",
         "\n",
-        "x_train_bin = np.array(x_train_nocon > THRESHOLD, dtype=float)\n",
-        "x_test_bin = np.array(x_test_small > THRESHOLD, dtype=float)"
+        "x_train_bin = np.array(x_train_nocon > THRESHOLD, dtype=np.float32)\n",
+        "x_test_bin = np.array(x_test_small > THRESHOLD, dtype=np.float32)"
       ]
     },
     {
@@ -1117,6 +1117,15 @@
       "display_name": "Python 3",
       "language": "python",
       "name": "python3"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.10.9 (main, Dec  7 2022, 13:47:07) [GCC 12.2.0]"
+    },
+    "vscode": {
+      "interpreter": {
+        "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
+      }
     }
   },
   "nbformat": 4,
diff --git a/docs/tutorials/noise.ipynb b/docs/tutorials/noise.ipynb
index 3d43d17e0..0a0ebc290 100644
--- a/docs/tutorials/noise.ipynb
+++ b/docs/tutorials/noise.ipynb
@@ -1,29 +1,4 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "noise.ipynb",
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.8.8"
-    }
-  },
   "cells": [
     {
       "cell_type": "markdown",
@@ -36,10 +11,12 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "cellView": "form",
         "id": "iiQkM5ZgQ8r2"
       },
+      "outputs": [],
       "source": [
         "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
         "# you may not use this file except in compliance with the License.\n",
@@ -52,9 +29,7 @@
         "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
         "# See the License for the specific language governing permissions and\n",
         "# limitations under the License."
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -102,29 +77,29 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "J2CRbYRqrLdt"
       },
+      "outputs": [],
       "source": [
         "!pip install tensorflow==2.7.0 tensorflow-quantum==0.7.2"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "QStNslxBwgte"
       },
+      "outputs": [],
       "source": [
         "!pip install -q git+https://github.com/tensorflow/docs"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
@@ -139,9 +114,11 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "iRU07S4o8B52"
       },
+      "outputs": [],
       "source": [
         "import random\n",
         "import cirq\n",
@@ -153,9 +130,7 @@
         "import matplotlib.pyplot as plt\n",
         "import tensorflow_docs as tfdocs\n",
         "import tensorflow_docs.plots"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -186,9 +161,11 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "Eu_vpHbfrQKQ"
       },
+      "outputs": [],
       "source": [
         "def x_circuit(qubits):\n",
         "  \"\"\"Produces an X wall circuit on `qubits`.\"\"\"\n",
@@ -202,20 +179,18 @@
         "my_circuit = x_circuit(my_qubits)\n",
         "my_noisy_circuit = make_noisy(my_circuit, 0.5)\n",
         "my_circuit"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "1B7vmyPm_TQ7"
       },
+      "outputs": [],
       "source": [
         "my_noisy_circuit"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -228,15 +203,15 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "0QN9W69U8v_V"
       },
+      "outputs": [],
       "source": [
         "rho = cirq.final_density_matrix(my_circuit)\n",
         "np.round(rho, 3)"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -249,15 +224,15 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "zSD9H8SC9IJ1"
       },
+      "outputs": [],
       "source": [
         "rho = cirq.final_density_matrix(my_noisy_circuit)\n",
         "np.round(rho, 3)"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -270,9 +245,11 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "Z4uj-Zs0AE3n"
       },
+      "outputs": [],
       "source": [
         "\"\"\"Sample from my_noisy_circuit.\"\"\"\n",
         "def plot_samples(circuit):\n",
@@ -285,9 +262,7 @@
         "  plt.bar([i for i in range(2** len(my_qubits))], freqs, tick_label=['00','01','10','11'])\n",
         "\n",
         "plot_samples(my_noisy_circuit)"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -300,15 +275,15 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "NRCOhTVpEJzz"
       },
+      "outputs": [],
       "source": [
         "\"\"\"Sample from my_circuit.\"\"\"\n",
         "plot_samples(my_circuit)"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -321,15 +296,15 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "D2Fg-FUdUJQx"
       },
+      "outputs": [],
       "source": [
         "my_really_noisy_circuit = make_noisy(my_circuit, 0.75)\n",
         "plot_samples(my_really_noisy_circuit)"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -355,21 +330,23 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "byVI5nbNQ4_b"
       },
+      "outputs": [],
       "source": [
         "\"\"\"Draw bitstring samples from `my_noisy_circuit`\"\"\"\n",
         "bitstrings = tfq.layers.Sample(backend='noisy')(my_noisy_circuit, repetitions=1000)"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "ncl0ruCZrd2s"
       },
+      "outputs": [],
       "source": [
         "numeric_values = np.einsum('ijk,k->ij', bitstrings.to_tensor().numpy(), [1, 2])[0]\n",
         "freqs, _ = np.histogram(numeric_values, bins=[i+0.01 for i in range(-1,2** len(my_qubits))])\n",
@@ -378,9 +355,7 @@
         "plt.xlabel('Bitstring')\n",
         "plt.ylabel('Frequency')\n",
         "plt.bar([i for i in range(2** len(my_qubits))], freqs, tick_label=['00','01','10','11'])"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -394,15 +369,15 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "ep45G-09rfrA"
       },
+      "outputs": [],
       "source": [
         "some_observables = [cirq.X(my_qubits[0]), cirq.Z(my_qubits[0]), 3.0 * cirq.Y(my_qubits[1]) + 1]\n",
         "some_observables"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -415,17 +390,17 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "jL6wJ3LCvNcn"
       },
+      "outputs": [],
       "source": [
         "noiseless_sampled_expectation = tfq.layers.SampledExpectation(backend='noiseless')(\n",
         "    my_circuit, operators=some_observables, repetitions=10000\n",
         ")\n",
         "noiseless_sampled_expectation.numpy()"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -438,17 +413,17 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "8U4Gm-LGvYqa"
       },
+      "outputs": [],
       "source": [
         "noisy_sampled_expectation = tfq.layers.SampledExpectation(backend='noisy')(\n",
         "    [my_noisy_circuit, my_really_noisy_circuit], operators=some_observables, repetitions=10000\n",
         ")\n",
         "noisy_sampled_expectation.numpy()"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -465,31 +440,31 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "pGXKlyCywAfj"
       },
+      "outputs": [],
       "source": [
         "noiseless_analytic_expectation = tfq.layers.Expectation(backend='noiseless')(\n",
         "    my_circuit, operators=some_observables\n",
         ")\n",
         "noiseless_analytic_expectation.numpy()"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "6FUkJ7aOyTlI"
       },
+      "outputs": [],
       "source": [
         "noisy_analytic_expectation = tfq.layers.Expectation(backend='noisy')(\n",
         "    [my_noisy_circuit, my_really_noisy_circuit], operators=some_observables, repetitions=10000\n",
         ")\n",
         "noisy_analytic_expectation.numpy()"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -510,16 +485,16 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "_ZqVLEji2WUx"
       },
+      "outputs": [],
       "source": [
         "qubits = cirq.GridQubit.rect(1, 8)\n",
         "circuits, labels, pauli_sums, _ = tfq.datasets.xxz_chain(qubits, 'closed')\n",
         "circuits[0]"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -532,9 +507,11 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "zkQofAqqGibQ"
       },
+      "outputs": [],
       "source": [
         "def get_data(qubits, depolarize_p=0.):\n",
         "  \"\"\"Return quantum data circuits and labels in `tf.Tensor` form.\"\"\"\n",
@@ -547,9 +524,7 @@
         "  labels_tensor = tf.convert_to_tensor([x[1] for x in tmp])\n",
         "\n",
         "  return circuits_tensor, labels_tensor"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -563,9 +538,11 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "TwryFaFIG2Ya"
       },
+      "outputs": [],
       "source": [
         "def modelling_circuit(qubits, depth, depolarize_p=0.):\n",
         "  \"\"\"A simple classifier circuit.\"\"\"\n",
@@ -590,9 +567,7 @@
         "  return ret, [op(q) for q in qubits for op in [cirq.X, cirq.Y, cirq.Z]]\n",
         "\n",
         "modelling_circuit(qubits, 3)[0]"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -606,9 +581,11 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "r09CT5N9DWa_"
       },
+      "outputs": [],
       "source": [
         "def build_keras_model(qubits, depolarize_p=0.):\n",
         "  \"\"\"Prepare a noisy hybrid quantum classical Keras model.\"\"\"\n",
@@ -624,9 +601,7 @@
         "  post_process = tf.keras.layers.Dense(1)(intermediate)\n",
         "\n",
         "  return tf.keras.Model(inputs=[spin_input], outputs=[post_process])"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -643,9 +618,11 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "QAgpq9c-EakW"
       },
+      "outputs": [],
       "source": [
         "training_histories = dict()\n",
         "depolarize_p = 0.\n",
@@ -659,15 +636,15 @@
         "\n",
         "# Show the keras plot of the model\n",
         "tf.keras.utils.plot_model(phase_classifier, show_shapes=True, dpi=70)"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "9tKimWRMlVfL"
       },
+      "outputs": [],
       "source": [
         "noiseless_data, noiseless_labels = get_data(qubits, depolarize_p)\n",
         "training_histories['noiseless'] = phase_classifier.fit(x=noiseless_data,\n",
@@ -676,9 +653,7 @@
         "                         epochs=n_epochs,\n",
         "                         validation_split=0.15,\n",
         "                         verbose=1)"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -691,27 +666,27 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "TG87YNUWKKLY"
       },
+      "outputs": [],
       "source": [
         "loss_plotter = tfdocs.plots.HistoryPlotter(metric = 'loss', smoothing_std=10)\n",
         "loss_plotter.plot(training_histories)"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "O2ZwM18YUxxm"
       },
+      "outputs": [],
       "source": [
         "acc_plotter = tfdocs.plots.HistoryPlotter(metric = 'accuracy', smoothing_std=10)\n",
         "acc_plotter.plot(training_histories)"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -725,9 +700,11 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "0jy54uWpgwhi"
       },
+      "outputs": [],
       "source": [
         "depolarize_p = 0.001\n",
         "n_epochs = 50\n",
@@ -740,9 +717,7 @@
         "\n",
         "# Show the keras plot of the model\n",
         "tf.keras.utils.plot_model(noisy_phase_classifier, show_shapes=True, dpi=70)"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -755,9 +730,11 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "210cLP5AoClJ"
       },
+      "outputs": [],
       "source": [
         "noisy_data, noisy_labels = get_data(qubits, depolarize_p)\n",
         "training_histories['noisy'] = noisy_phase_classifier.fit(x=noisy_data,\n",
@@ -766,31 +743,29 @@
         "                         epochs=n_epochs,\n",
         "                         validation_split=0.15,\n",
         "                         verbose=1)"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "eQ8pknNdohzy"
       },
+      "outputs": [],
       "source": [
         "loss_plotter.plot(training_histories)"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "id": "nBtgnKWtuWRR"
       },
+      "outputs": [],
       "source": [
         "acc_plotter.plot(training_histories)"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -801,5 +776,30 @@
         "Success: The noisy model still managed to train under some mild depolarization noise. Try experimenting with different noise models to see how and when training might fail. Also look out for noisy functionality under `tfq.layers` and `tfq.noise`."
       ]
     }
-  ]
+  ],
+  "metadata": {
+    "colab": {
+      "name": "noise.ipynb",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8.8"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
 }
diff --git a/docs/tutorials/qcnn.ipynb b/docs/tutorials/qcnn.ipynb
index f6d851723..f53182701 100644
--- a/docs/tutorials/qcnn.ipynb
+++ b/docs/tutorials/qcnn.ipynb
@@ -12,12 +12,15 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "cellView": "form",
         "colab": {},
         "colab_type": "code",
-        "id": "iiQkM5ZgQ8r2"
+        "id": "iiQkM5ZgQ8r2",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -91,11 +94,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "Aquwcz-0aHqz"
+        "id": "Aquwcz-0aHqz",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -114,11 +120,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "3Pl5PW-ACO9J"
+        "id": "3Pl5PW-ACO9J",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -127,11 +136,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "4Ql5PW-ACO0J"
+        "id": "4Ql5PW-ACO0J",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -152,11 +164,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "QytLEAtoejW5"
+        "id": "QytLEAtoejW5",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -201,11 +216,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "FhNf0G_OPLqZ"
+        "id": "FhNf0G_OPLqZ",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -240,11 +258,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "ImRynsUN4BSG"
+        "id": "ImRynsUN4BSG",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -263,11 +284,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "tfff6dJp39Fg"
+        "id": "tfff6dJp39Fg",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -338,11 +362,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "iUrvTCU1hDgP"
+        "id": "iUrvTCU1hDgP",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -380,11 +407,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "eLJ-JHOihDgT"
+        "id": "eLJ-JHOihDgT",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -419,11 +449,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "qpQwVWKazU8g"
+        "id": "qpQwVWKazU8g",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -448,11 +481,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "9tZt0aAO4r4F"
+        "id": "9tZt0aAO4r4F",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -476,11 +512,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "oNRGOqky2exY"
+        "id": "oNRGOqky2exY",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -532,11 +571,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "T5uhvF-g2rpZ"
+        "id": "T5uhvF-g2rpZ",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -555,11 +597,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "aJTdRrfS2uIo"
+        "id": "aJTdRrfS2uIo",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -578,11 +623,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "DOHRbkvH2xGK"
+        "id": "DOHRbkvH2xGK",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -603,11 +651,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "1Fa19Lzb3wnR"
+        "id": "1Fa19Lzb3wnR",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -636,11 +687,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "Bi6q2nmY3z_U"
+        "id": "Bi6q2nmY3z_U",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -662,11 +716,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "jD3fgcWO4yEU"
+        "id": "jD3fgcWO4yEU",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -693,11 +750,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "pFXow2OX47O5"
+        "id": "pFXow2OX47O5",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -721,11 +781,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "vzEsY6-n5NR0"
+        "id": "vzEsY6-n5NR0",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -785,11 +848,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "_TFkAm1sQZEN"
+        "id": "_TFkAm1sQZEN",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -820,11 +886,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "2tiCJOb5Qzcr"
+        "id": "2tiCJOb5Qzcr",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -875,11 +944,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "Ut-U1hBkQ8Fs"
+        "id": "Ut-U1hBkQ8Fs",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -932,11 +1004,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "EyYw9kYIRCE7"
+        "id": "EyYw9kYIRCE7",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -955,11 +1030,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "yL3jhGiBRJHt"
+        "id": "yL3jhGiBRJHt",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -1008,11 +1086,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "W3TkNVm9RTBj"
+        "id": "W3TkNVm9RTBj",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -1065,11 +1146,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "suRvxcAKRZK6"
+        "id": "suRvxcAKRZK6",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
@@ -1089,11 +1173,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 0,
+      "execution_count": null,
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "-6NR7yAQRmOU"
+        "id": "-6NR7yAQRmOU",
+        "vscode": {
+          "languageId": "python"
+        }
       },
       "outputs": [],
       "source": [
diff --git a/docs/tutorials/quantum_data.ipynb b/docs/tutorials/quantum_data.ipynb
index 58329cd0f..9e78b9493 100644
--- a/docs/tutorials/quantum_data.ipynb
+++ b/docs/tutorials/quantum_data.ipynb
@@ -114,21 +114,21 @@
     "!pip install tensorflow==2.7.0 tensorflow-quantum==0.7.2"
    ]
   },
-    {
-      "cell_type": "code",
-      "execution_count": 0,
-      "metadata": {
-        "colab": {},
-        "colab_type": "code",
-        "id": "4Ql5PW-ACO0J"
-      },
-      "outputs": [],
-      "source": [
-        "# Update package resources to account for version changes.\n",
-        "import importlib, pkg_resources\n",
-        "importlib.reload(pkg_resources)"
-      ]
-    },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "4Ql5PW-ACO0J"
+   },
+   "outputs": [],
+   "source": [
+    "# Update package resources to account for version changes.\n",
+    "import importlib, pkg_resources\n",
+    "importlib.reload(pkg_resources)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -285,7 +285,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -484,9 +484,7 @@
    "outputs": [
     {
      "data": {
-      "image/svg+xml": [
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@@ -559,9 +557,7 @@
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text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">Rx(0.5π)</text><circle cx=\"530.091796875\" cy=\"25.0\" r=\"10.0\" /><rect x=\"510.091796875\" y=\"55.0\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"530.091796875\" y=\"75.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"18px\" font-family=\"Arial\">X</text><rect x=\"570.091796875\" y=\"55.0\" width=\"91.64871093750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"615.91615234375\" y=\"75.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">Rz(2.0*ref_0)</text><circle cx=\"701.7405078125\" cy=\"25.0\" r=\"10.0\" /><rect x=\"681.7405078125\" y=\"55.0\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"701.7405078125\" y=\"75.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"18px\" font-family=\"Arial\">X</text><rect 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x=\"936.1764648437501\" y=\"75.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">Rz(2.0*ref_0)</text><circle cx=\"1022.0008203125001\" cy=\"25.0\" r=\"10.0\" /><rect x=\"1002.0008203125001\" y=\"55.0\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"1022.0008203125001\" y=\"75.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"18px\" font-family=\"Arial\">X</text></svg>",
       "text/plain": [
        "<cirq.contrib.svg.svg.SVGCircuit at 0x7f6db41ccbe0>"
       ]
@@ -1047,7 +1043,7 @@
     },
     {
      "data": {
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\n",
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",
       "text/plain": [
        "<Figure size 720x360 with 1 Axes>"
       ]
diff --git a/docs/tutorials/quantum_reinforcement_learning.ipynb b/docs/tutorials/quantum_reinforcement_learning.ipynb
index 5a59f4080..fba0291e6 100644
--- a/docs/tutorials/quantum_reinforcement_learning.ipynb
+++ b/docs/tutorials/quantum_reinforcement_learning.ipynb
@@ -177,7 +177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": null,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -360,9 +360,7 @@
     },
     {
      "data": {
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fill=\"white\" stroke-width=\"1\" /><text x=\"324.0572070312501\" y=\"125.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">Rz(theta8)</text><circle cx=\"404.74449218750004\" cy=\"25.0\" r=\"10.0\" /><circle cx=\"404.74449218750004\" cy=\"75.0\" r=\"10.0\" /><circle cx=\"464.74449218750004\" cy=\"75.0\" r=\"10.0\" /><circle cx=\"464.74449218750004\" cy=\"125.0\" r=\"10.0\" /><circle cx=\"524.7444921875001\" cy=\"25.0\" r=\"10.0\" /><circle cx=\"524.7444921875001\" cy=\"125.0\" r=\"10.0\" /><rect x=\"564.7444921875001\" y=\"5.0\" width=\"62.45953125\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"595.9742578125001\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">Rx(x0_0)</text><rect x=\"564.7444921875001\" y=\"55.0\" width=\"62.45953125\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"595.9742578125001\" y=\"75.0\" 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y=\"105.0\" width=\"91.21832031250001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"692.8131835937502\" y=\"125.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">Rx(theta15)</text><rect x=\"758.4223437500002\" y=\"5.0\" width=\"90.4483203125\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"803.6465039062502\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">Ry(theta10)</text><rect x=\"758.4223437500002\" y=\"55.0\" width=\"90.4483203125\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"803.6465039062502\" y=\"75.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">Ry(theta13)</text><rect x=\"758.4223437500002\" y=\"105.0\" width=\"90.4483203125\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"803.6465039062502\" y=\"125.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">Ry(theta16)</text><rect x=\"868.8706640625002\" y=\"5.0\" width=\"90.28179687500001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"914.0115625000002\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">Rz(theta11)</text><rect x=\"868.8706640625002\" y=\"55.0\" width=\"90.28179687500001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"914.0115625000002\" y=\"75.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">Rz(theta14)</text><rect x=\"868.8706640625002\" y=\"105.0\" width=\"90.28179687500001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"914.0115625000002\" y=\"125.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">Rz(theta17)</text></svg>"
-      ],
+      "image/svg+xml": "<svg xmlns=\"http://www.w3.org/2000/svg\" width=\"1009.1524609375002\" height=\"150.0\"><line x1=\"34.7588671875\" x2=\"979.1524609375002\" y1=\"25.0\" y2=\"25.0\" stroke=\"#1967d2\" stroke-width=\"1\" /><line x1=\"34.7588671875\" x2=\"979.1524609375002\" y1=\"75.0\" y2=\"75.0\" stroke=\"#1967d2\" stroke-width=\"1\" /><line x1=\"34.7588671875\" x2=\"979.1524609375002\" y1=\"125.0\" y2=\"125.0\" stroke=\"#1967d2\" stroke-width=\"1\" /><line x1=\"404.74449218750004\" x2=\"404.74449218750004\" y1=\"25.0\" y2=\"75.0\" stroke=\"black\" stroke-width=\"3\" /><line x1=\"464.74449218750004\" x2=\"464.74449218750004\" y1=\"75.0\" y2=\"125.0\" stroke=\"black\" stroke-width=\"3\" /><line x1=\"524.7444921875001\" x2=\"524.7444921875001\" y1=\"25.0\" y2=\"125.0\" stroke=\"black\" stroke-width=\"3\" /><rect x=\"10.0\" y=\"5.0\" width=\"49.517734375\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"0\" /><text x=\"34.7588671875\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">(0, 0): </text><rect x=\"10.0\" y=\"55.0\" width=\"49.517734375\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"0\" /><text x=\"34.7588671875\" y=\"75.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">(0, 1): </text><rect x=\"10.0\" y=\"105.0\" width=\"49.517734375\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"0\" /><text x=\"34.7588671875\" y=\"125.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">(0, 2): </text><rect x=\"79.517734375\" y=\"5.0\" width=\"82.31109375000001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"120.67328125\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">Rx(theta0)</text><rect x=\"79.517734375\" y=\"55.0\" width=\"82.31109375000001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"120.67328125\" y=\"75.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">Rx(theta3)</text><rect x=\"79.517734375\" y=\"105.0\" width=\"82.31109375000001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"120.67328125\" y=\"125.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">Rx(theta6)</text><rect x=\"181.82882812500003\" y=\"5.0\" width=\"81.54109375\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"222.59937500000004\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">Ry(theta1)</text><rect x=\"181.82882812500003\" y=\"55.0\" width=\"81.54109375\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"222.59937500000004\" y=\"75.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">Ry(theta4)</text><rect x=\"181.82882812500003\" y=\"105.0\" width=\"81.54109375\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"222.59937500000004\" y=\"125.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">Ry(theta7)</text><rect x=\"283.36992187500005\" y=\"5.0\" width=\"81.37457031250001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"324.0572070312501\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">Rz(theta2)</text><rect x=\"283.36992187500005\" y=\"55.0\" width=\"81.37457031250001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"324.0572070312501\" y=\"75.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">Rz(theta5)</text><rect x=\"283.36992187500005\" y=\"105.0\" width=\"81.37457031250001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"324.0572070312501\" y=\"125.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">Rz(theta8)</text><circle cx=\"404.74449218750004\" cy=\"25.0\" r=\"10.0\" /><circle cx=\"404.74449218750004\" cy=\"75.0\" r=\"10.0\" /><circle cx=\"464.74449218750004\" cy=\"75.0\" r=\"10.0\" /><circle cx=\"464.74449218750004\" cy=\"125.0\" r=\"10.0\" /><circle cx=\"524.7444921875001\" cy=\"25.0\" r=\"10.0\" /><circle cx=\"524.7444921875001\" cy=\"125.0\" r=\"10.0\" /><rect x=\"564.7444921875001\" y=\"5.0\" width=\"62.45953125\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"595.9742578125001\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">Rx(x0_0)</text><rect x=\"564.7444921875001\" y=\"55.0\" width=\"62.45953125\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"595.9742578125001\" y=\"75.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">Rx(x0_1)</text><rect x=\"564.7444921875001\" y=\"105.0\" width=\"62.45953125\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"595.9742578125001\" y=\"125.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">Rx(x0_2)</text><rect x=\"647.2040234375002\" y=\"5.0\" width=\"91.21832031250001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"692.8131835937502\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">Rx(theta9)</text><rect x=\"647.2040234375002\" y=\"55.0\" width=\"91.21832031250001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"692.8131835937502\" y=\"75.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">Rx(theta12)</text><rect x=\"647.2040234375002\" y=\"105.0\" width=\"91.21832031250001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"692.8131835937502\" y=\"125.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">Rx(theta15)</text><rect x=\"758.4223437500002\" y=\"5.0\" width=\"90.4483203125\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"803.6465039062502\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">Ry(theta10)</text><rect x=\"758.4223437500002\" y=\"55.0\" width=\"90.4483203125\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"803.6465039062502\" y=\"75.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">Ry(theta13)</text><rect x=\"758.4223437500002\" y=\"105.0\" width=\"90.4483203125\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"803.6465039062502\" y=\"125.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">Ry(theta16)</text><rect x=\"868.8706640625002\" y=\"5.0\" width=\"90.28179687500001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"914.0115625000002\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">Rz(theta11)</text><rect x=\"868.8706640625002\" y=\"55.0\" width=\"90.28179687500001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"914.0115625000002\" y=\"75.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">Rz(theta14)</text><rect x=\"868.8706640625002\" y=\"105.0\" width=\"90.28179687500001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"914.0115625000002\" y=\"125.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">Rz(theta17)</text></svg>",
       "text/plain": [
        "<cirq.contrib.svg.svg.SVGCircuit at 0x7f3fbcc9e730>"
       ]
@@ -591,7 +589,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -864,7 +862,7 @@
     "    actions = np.concatenate([ep['actions'] for ep in episodes])\n",
     "    rewards = [ep['rewards'] for ep in episodes]\n",
     "    returns = np.concatenate([compute_returns(ep_rwds, gamma) for ep_rwds in rewards])\n",
-    "    returns = np.array(returns, dtype=float)\n",
+    "    returns = np.array(returns, dtype=np.float32)\n",
     "\n",
     "    id_action_pairs = np.array([[i, a] for i, a in enumerate(actions)])\n",
     "    \n",
@@ -908,7 +906,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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y1Oikkfajk4qqdgpZfIlxXldXl+Vpp5B1E7I6cW+AboVszEuNPcGwx51CFpYsfTCsV8joN+R+t5RC1kSFkIeMm/rDcuiVDiFkAoFAIADQREr8+O98JJk0H4OrN1S2JJ+XUsqVrtz+e8oh4wqZ3TbgeKkuS9/haDv+5gXDzlHISCkkUqQ6TP1ETILYC/sEzjPlrqcx9Wtkuk3sOCns67I8tW7nWQ5QyOLGAVIMWwqZJVS7RVMq7Db1k0IWlqpjhWxW1u7eX5xVQSxIenRSc3xesoynAFzpWI2rEAgEAsGecec9j+FX3vmpuaoREBOyUCHLdFOy5FhWITO2xOc9ZKS4zV+jSZQs6bVSCpORDmI7UhgyOomXDLtGJ5HyFMReBF2WPPaiKVnGhOzczvAuSwC9nZZdSf1eIYtyyKwytWWJaTzrknDcmfrDLksfDOvjRmZV7fenCQQm7SGLg2FDU78oZAKBQCBYIVQJAjNvX4ApZFHJkmPZtP0yIg60NBr63QfaN+UhcyXLgaOTtqZlMvC1qkNClPJ+BdeRjL3wwbCU1N+ULLsJWd/opJOWkPVlkS2aQ0aK2IVpEbyPsTnOoVS7ZOkUMvu97VmJqja45vgEgC+vhkn9/cGwRB4lh0wgEAgEK4VF/F48xoLKXNwbdWKfFLLYB2YWUMhSafRcVWpCSueZ+ptcMGOAi7N22bI2JigZdpUsuc8ubgTIuUJWNkn9KYUsTrR3/rVIITo+yZFp1VuydOXc6DZ25pBZIkREqyupX2uFYxM/PskHw/qkfn6cay0h4wHC8SzLoMuSzbL0/jRRyAQCgUCwQqCH5yCFjD3Id2ZhyVJFCtnmOFvaQxY3DPguyyEKWdu4HnZZ9pv6jTGYVTWussPSUz4y7nlqjpsuWdJ6Oblwo5O0ctEeRV0PUsh4GS8WiBR1ufbEXnSNTpp1dFkSAaOSZZepH2iiL+KSZZxD5gnZWnBtnOCmRieNM1aydKGzq0FlVuMqBAKBQLBneIVsQQ+ZVZl4DhknZKc3x3tWyIg3EBEc0mXpv8NLls3fpmSZ9RIy6uI7s9moOF2EjAtUXaOTaL3kiaN9AW9KbzpWjfeQJbos16xCVNdeaYtLlkATDvt0n6m/o8ty2pNDBviSZVcOGRAOGI/nTXqFrDmOU8i2Wckyaer3HZVaSQ6ZQCAQCFYY5UIKWcpD1rynYNjmdaOWLZvUTw/d2PO0mELWLllqRQOzm7U/sTVtecRInTtzjBSyNsFpKWRdXZYs9oKWo1nJEmhIWzPLUSdHJ53fLXHaZoz1mfqBxke2TA5Zl0I2XlAhc12WLpqCYi8aIrc1tQrZiZ6SpTP1hwG8eaZRWlN/ppXz4l3pEEImEAgEAgBMIRtAdkJTP81p9HEO66MMo0zh+NoII5ZEv+yaWiXLQR6y8BjhGm2XZVHj/G6BF/3k2/D7H344+D6RkzO2ZJnqtGy6LP2jVKt2lAQQEssqLllahWdn5iMnUiXL8zuFJ2TBcdqE5PTGuL9k2aWQdZn67XsK+u0y9QONQkZTBboUMjqOM/WTQmb8cHG6rbxkqZVyJd6yMivTYQkIIRMIBAKBxWIeMk+ISCHjBIF8TCfXR8gT5GIoClfqa977LssBhCxxPVxpIVP/k1sz7BY1Hjm3mzz3VT0ly7o2gYdLK5X0kAWm/ij2gkjFtiVkuVbJgNlzO4Xzs6WaAzhOrY/6c8jo3nTFXmRhSdJ1WQ5QyI6v5a60GQ8Xn0Qly41xjs1x5j1kNVhSf9vU7xQyW7JclQwyAMgPewECgUAguDzAoxfmgVu4iJC5Upx9oJ5YH2Fz3HT87b9CNv94tB7OObjSQrEXVIbjczkB7/uikuX5LlO/ikqWCa7oCBkLdHWxF5ak7Njzp4aLG2MahWyTFDL0KmQ03L0L3aOTmjXEOWMuh8x5yPoI2agz9iI29U9yjRPrvgmgYgTX5ZDxjlKl7KgpA6BemZR+QAiZQCAQCCxSilIXQlN/e3QSAFx3Yg0b4wy7Re3IxqKg0mTtCITdvsgsy2RSP82yrJ1aE68xLll2ecj46CKVULbiWZyusUD70UmAV8hGmWqVLLemJcra4CqbMVazY6aaDCcj3asizguGbcVeZMM9ZGTqN6bt84pN/eNcY5xr93vyoN1UDpnWTVdls79emQ5LQAiZQCAQCCyWNfVPI1M/PXx/+ltuh9bAD/2XDy2tkPlg2PC8s0VM/YGHrPnLTf0XpqSQhQSGfF+ND06luyxbSf1tU3/ByGMq9iKPPGSkkHGy9OmntgEAzzmz6Y9D6lNCIRuzbLMU6PalcshyrVrzMZ2pfzrA1L8+QlUb7BQVyjr0ebluTaaQcQW1rtlwcdVVsqT965XpsASEkAkEAoHAgnxh+5HUDwDXnWwyphoP2ZJJ/Yx4GfZgHqaQtdfKy3w0XJzIQayQFczgfpx1DnLEXZaZbsdecGIUxF6QQpZRybJZR57ZLkv2O9z/ZEPIbrlm0523r8tylGnUpr0+Qp9CliJbsRm/v2RJ8yzLlvG+HTCbBR5DTnCJZ7ZM/Y5s6pUqWa6O1icQCASCPYG4z7AcMm7q9xlbQNvTlGk1qHMzBU5masOGiw/ykLUVP26En4wyGAM8dXFqryMkZFMWAbE+ylxJkSNO6k+Z8fm1V3Xba0cqT1yy5Jf4yScuAgBuufqYOy8PuY1BJK9LJYvz3fg1p8hWW9nqziHbHDeE7OK0GY/ERxtlWiHXKlDaMu27cKuKEzJqbuBkv/GQlZVp5n6uUMlyda5EIBAIBHvCQgpZwtRPHC1WZPJs76Z+oHkoE8ka1GWZMvUHXZbNI/DJrSYeIp5rSWRmlKlG+UpcQ6xApUYnhaSyHXuRtzxkGplCpJBdxDXHJ0594qpRqmRJJI8H6L7nk0/h/73rweA+DFXIXFL/1Hu/uuDJYOMhi6Mpxrl2amNTsgy7Pvn1UB5bULLUTZdlWRlRyAQCgUCwelhkliUnJ5TUTw/5mB9kWu8h9iIkZIvlkCViL6IuSwB4fKtRyLpM/eNMI+/oFK2Y56k5brtzsYi6JeOh4EQqdlmXZWzq/9ST27jpzIZTw+Kw1BhEmApGXH/h7ffhp9/ysebedOSQzaqOkmVs6u+JmyAyWFRp0jTOtc8zixWyVNBuR5dlQ/ZWh8aszpUIBAKBYE9YJIeMxxnMK1k2ZGY5D1kY/cBKlguY+rmHjF5SyRLwCllcsuQespQ3jNbXNvVHhIyRoorFXtB9InLjcsgSw8Xvf/IinnNmM+g87BudxFUqwr2PbbXyx+gcr3/HJ/CXZ3cahSxBtrQtNQ7JIaPPZnYAeEyaJrnvACUPGfe08RIseel46HCYQ7Y6CpmY+gUCgUAAYMEcMvsgb2It2qOTODKtlh8uXoflPnowLzLLsk4qZL4M98RW2kPmS5a60wdXmVChSiXsl/E1MFII+LFCRMjGWUgAt2clHj0/xU1nNoLOw6qjRExr5tewPSvx0NkdN5eT57ud2y7wL37/btTGWA9Z2h82yTUu2jX2mfqJ0BVljbKqW+vjZG7suix9uTwoWSoVDVJviKHPIVsdXWl1rkQgEAgEe4JXyAaY+u2D/tgk96OTIm8UoavcN2hNQcmSBcMOGe+USKMPuywb4vGEK1mG181N/XHXo1tTSyFLlAHLdNm1HXthuyw15ZA1n1OH5U1Xb7rMsdQIJo7YQ/aJxy9ahTFUxmp2nPO7BaZl1al+0Xat0EuERrFCFqlYnPBRObiqjS3nhk0KWquWX26UNTlkhYxOEggEAsEqYrFZls3fjXHm0t3jHDJCF5kZAq4u8YDVRTxkYQ4Z77KMTf2xQtbs60hDomRZ1nUrqT/2kLUUstpAKX+fxi72wnrI8jCH7P4nmw7Lm85sunNRyVKr9v3mxySF7N7HLjRrqbwS1azHvz63U3Sa+gFPyPrKlUCozqXmTdLalPINEyUjXXz/2NSvWQ5ZWdUrNTppda5EIBAIBHtCWYfqSR+I2GxOcjY6KW0y34tCxr/HDfFcdepcown/AukuSzpHVw7ZKJEL5s5Rh0n5c4Nh62Y9vKybx7EXOjzfp6xC9uwzG36ckC3jpcqVtGYAKOx9uu+xLXt+Iql2/axz9fxOiVmVjr0AGCGbQ4Lo81lp0h6ykT+OUsopZKkmBa0apZArZLnW1tS/Wh4yIWQCgUAgAOBJ1hDyRPtsjn3JkvuzOPJs+S7LMipZutFJAxSyZA4Z87nFXqnWLMuoZJn2kJm5HrK4U7QyJrhHRFhcUn+uXHch0ChkZzbHOLE2cuU8r5B1EDJWNgSAex9tCJnrZmSjqHjJctaRQwb4UiM1Q3RhnLMuy7o9b5IIG52H7q2fosAVsnh0EuWQ2ZKlKGQCgUAgWDUsMjqJlCqukHWZ+vfSZVnWIZlZxENGXw1mWTLSuBYN0G6PTgpzyFL3pemy9MdJxl7wHLK6uQZ+j+Jg2FzbbC57nMcvTHHtiTW3Py/jdRIyFj0BeIWs5SFjTQbndwpM+0qW2TCFbOQUsroJhu0w9Y8twctaCpnfl0z94egkzUqWopAJBAKBYMWwCNmhfY5NMpdDRt9v55DtwUMWh6ou0GXpB5K3uyybkmWo9OwUVbDvjJUsc62THrImqd+/T5UsY5UvVrZGkYdsHOWQlbXBmJ0kc2W8dIclHQNoCNm0rHD/U9vQipWlmYeMXp/fLTtjLwBPpPo6LPn1eA9ZHHuRBcehe8sz4gje1O+vfaSVVd/ax76SsTpXIhAIBII9gYjDIrMs18c5dmZDcsj2w0PGc8iWC4bl5nBOLE6ujwD4zkogDIbVHddQVqkuy3C/WUQqaxMSqXi4eJ6pIM+sqk3UeejVwq4mQ06KPvXENqra4OarN5t7yNQorjqe3ydTvwultSXLmDRO8nbJkmeNxbNBQ1N/c3+Kqm4GoYtCJhAIBIJVw6JJ/ZlWWBtp153YnUOmHRFYFLFCRqRv2CxL+l7bT5Yp32UJANccb/K5uI+sqGoXQdE1ID0uG6ZGJ5WJaQP8Fo3c6KQm9qIZncQUsqhTkT7rGhxOxwAaY/3D53YAAM850wwmL5nixP1Z53cLa+rvziHjf7vgzm2N9+3Yi5DYUUk7Zern10rv80yjtEn9MstSIBAIBCuHypGW+epTaQM810btkmX8jKQH8jIqWegh8+cYNsuybebnoayceFx9bAwg9JHNSh+rkLFcMI6YFGWqPfOy5SGLvkMG/J3EcHFj2sSLypl9XZbcWE+q3+Ykd/clKFna5e4WNbZ2y04FLCZSXRgzD1ky9iKlkFUmKCfza+Wl6kyzkqXMshQIBALBKmIhhcySgbU8Q2Efpt0KGRGyxY39ASELcsiGm/qBMJkeaHxuXOm55nhjmg8VMhOoOCmi2u6ybJcsg9gLk4i9sN/fLvhwceWuIZ7ZSCn+vV2WgYfMErJxQ0DLjpIl0D3LElgkh4x3WbY7IVsKWRbmkIW5bpQ/R7+bN/UXK5ZDJqOTBAKBQADAE6ZBsywrX7IEGiLTNVsx13tQyBiZ4UnzxQIeMv6aP/Qzpq5cc6wpWfIsshl74Hd5yGK1S6VmWbKSpUnEXtA5glmWPJE/ocINLVkWVe2I6bolZFXlFTITETKgu4tyaJdlnmloRab+ukcha9ajo7Jk4JezJeCaEd888xEkktQvEAgEgpUDCUBDYy+a6IjmobpbVJ2jk+hBusw8y67Yi2LAsUwfIdMKSnlj/9XHqWTJCFlZu+5GPgA7Xl87qT/eJ5w2YCLfWbMWX4Ydae3zxgyFq8ZlPFhiN8dDVhk3SWHDKWQ1U8jav/dk1FWyzIK/fRhlGrPKxl5EBC5W2qjpw0VbRPeGuixp+0hrFDWVLFeHxqzOlQgEAoFgTyDiMGi4uH3QrhMhK70SE4/y2ZtCFhGymrYPUMjYLrxkyccWESEjhYx7yApWvst6FLJQ0Ul0WZa8McF2TUb3iHvVtFZ+iHiHQpbyonHwAd9Te00b46YoxiMm+D2Nv9s65sCSJR1jVtYo6rZCFsdeZFqH8yo7uiypajvKmiaRmeSQCQQCgWAV4YeLDyhZWlIxYSVL06mQ6cHHTZ2HwE39C5cs2aggrsBQ6nxXl6UjSqojGDY6HgWZdl9DO/YCAEbaK3EAUxUteaFBpxQAACAASURBVOLmdSIpVeI47ngpUz+VLOvY1B8pZHv0kDXn1yiqGlWPqT/2kPFIEgIvZ9J95vdCcsgWgFIqU0q9Xyn1u/b9zUqpdyul7lNK/YZSamy3T+z7++znNx302gQCgUDgwU39T25N8ZKfuhP3PnohuW9ty2g5I1u+ZBkpZK7LcglTfyv2onk9pGQZmPpdyTJ84LuS5bE2IeOZXHnWldQfHk8p1VKcijLqsoxiL5rjh/4suoe1U8jYNAAdDhdPgXvISKEjhaysDBudZFoEcq+mfrqOouyPvYhzyIi4xsPFqcuS7jNXxaTLcjF8L4C72fufBPAzxpjPBPA0gO+0278TwNN2+8/Y/QQCgUBwieBHJ9V48OkdfPqpbXz88a3OfTMdms95pAQHPWD3rpCx0UkLzLKk79JfvrxJ3pRdj9lIiNDUb6LYi0TJ0kT+rtTopFjlS5Qa6TxEMDJ2z1oeMqvC9Zn6aX/ykI0y5Y7dEBy//ni9e80hAxqFjros4zWmPWR1MqmflMnA1M/IqZQsB0IpdSOArwPwy/a9AvBSAL9pd/lVAN9kX7/Svof9/GUqNiIIBAKBYF/w3k89hZf/63e47CsgHC4+z09Gnh4deZ2A9OikvmNx/NI7Po7v/U/vd+858aprXrJcTCGrHdkMS4xrowzH13LWnMA8ZGyMUK51R1J/mESfGp1UMK9TZUuWbQ8ZqT++q5OuobPLssfUr5RqVCpbspzkWfA71EwxjKu/+6GQjTKNqeuy7Bqd5LsseXNBK0akDn+3kZQsl8K/BvCPAdDPfQbAWWNMad8/COAG+/oGAA8AgP38nN1fIBAIBPuM93/6adzzyAU8eXHqtjmFrDKYlf1+smZotHYPT660tGMvhnvI3vXxJ/HeTz4VnIdQW98UsLiHzJcsTatkeWwtD+I7CDyTS3d4yGJypTXaHjLrRSP1LI69ALwyxj1rdA1lZIynsNS4WzPGKFONqb+sMM518DsEpv4FYy8mAzobm5Jl3VL3gLRCBvjmh2C4eGDqJw+ZKGQLQSn19QAeM8bctc/H/S6l1PuUUu97/PHH9/PQAoFAcGTw9HYBIOxirFIKWYcSVVn/kvM6mb7RSar3WBxPbs0wY/uFGV6+HDjkWAEhYwSEKzDr4wwn1kZOIdtpmfq9kTztITMBgWhiL9o5ZLlWjtSliBSNABq5kqU//jI5ZIA31s/KGpNcu2OWcQ5ZdF17TeqnfbqCYeM8M8qDo5mfAcFNmfqDOaCro5AdZDDslwH4RqXUKwCsATgB4GcBnFJK5VYFuxHAQ3b/hwA8C8CDSqkcwEkAT8YHNcb8EoBfAoA77rhjcUOCQCAQCHCWCBnPyGIqEilQ8xQyzbxOXTlki3jIntiaYlZ6UhQrZIt0WXJe1NVl+YNf/Vkoa4NJrqEU3FxOIDU6Kd1lmQoy5aD4DIoG6Yu9yCNTf9JDZsc4VSZsKIjRZIEZW7LUrjGgNibIIYvX2+URWzSHrKhMMhiWOnPpb1shS5n6/Xaezi/BsANgjPkRY8yNxpibAHwrgLcZY/4WgDsB/HW727cD+G37+k32PeznbzPx/8wQCAQCwb7g7PYMAFxpsq59B2NljFOm4vIbobSlP15a68ohywZ2WRpjrEIWZoERghyyum1GjxGTuWZbuL7Pe/ZpfOFNV0GpZgzULuuI5CXLTKnk+uuYLCVyyArro6LP6gSRapUsWSm4qsIuy0z7eZR9FTvnISsaDxnPg+OKIf3GFBy7Px4yhWlZoTbtTshJrJDZa0sSMmbqp1vAj7dKo5MO40p+GMAPKKXuQ+MRe4Pd/gYAZ+z2HwDw2kNYm0AgEBwJxAoZN6xXlVfIaPsHHjiLR87tun2a0p8fJE6luFQJbWgw7IVpiVlVB2XKMBg2JDvzjpfykNVRiZFjbaSDJoeiqhlp8MO+OeKkfpUYLl5WBqNcBYGubQ9ZXLKMFLKsTVLmliwzZU39jYfMH7NmXaf+Pp1aHwHYp9iLPPOjoDoVsiz4nPLSgskHOlWyZArZCnnILsksS2PM2wG83b7+BIAvSuyzC+BvXIr1CAQCwVHH01YhI/ITEx0iQpUlZv/w39+Fr37+dfixb3x+s91mYzmFzD40U/xgqIfsiQtTd2wiG5x0GZZD1qy9f7h0WLL0il/WYYRfG2WtHDJXSmQEycVHUEzDnJLlrKqbcUjKG9S7gmHpfIopjzHxIlN/X5clHauoaswqKln634EUMu4hO7Uxxl+e2+0Ohh04y7LZRzk/XhZ1Qo4zW/qM1MAuhcxdayqHTLosBQKBQHAl49xOo5DFShjQKCizaPt2UQXqUWXLZTxRvjbtciUwvMvyyYsz95oezkHshQlLqPOiL7oUsi7f1fooi0z9xndZJlQ+OmY4e7FdsixtnplSNCC9fZ9i4udHJ6HVZenKeHMVMo1ZaZqS5Ui3fiu6R/T61EajkHXmkEXerz6MMo1d++8l7oRci47jCBmZ+uPRSXStLqlfuiwFAoFAsCIghcwpYXWHQsaiMELS1pj6FTOfm0ScA8BzyPo9ZKSQAf7h3GXqb9YeHu+f/rcP49ff/Wm2v/+sq8uSYzLKWjlkMVFKdW62FbKEhyxTzqDeNBaE5ybVzc/OtNdY161RS66M13MtgO+ynNo8NbqGIioDV04hm1OyXEAhG2Ua204hC9f4Gdccw49+3WfjpbddG3w+S5QsPYn1auCIHW+VuixX50oEAoFAMAi7ReWIR6qbMuiydMn4BhXvyKwbkzU9TOOHJsfQLssnEgpZbOoPS5bh8f7w7kfxZ594Mtif4BsW2rEchLWRxpR1eE6j4eJArCSa4PoA6yGLuyxrY3PImEF9jkJGn6dICpXx6kS3JseYech4MOwsGuVEvrjbrjuBUxsjnLReshg3nF7HONN49pmNznO6c+fae8gi0qS1wt978S04vjYKrpl+69RwcT6ialUVskviIRMIBALB5QMy9AO8ZOkf0iUnZJVxn8fluhHzkNHopCQhy9pkJoUuhawxpxtH+uK1E6ZlHVyHSahZffMf10eZK8saY6ypP8y+qqrwHgCReqUSo5PKJs9Ma59IPy+pv6UaRcPFZxXmK2QZyyFjJcuY5NJ1fM3zr8NrXvqZybIzANx4egMf+xdf23m++Ny09nnRFLQuMvV3j05qtkmXpUAgEAhWAlSuBDxJ4tXERiEzwed83A7tk2ntuyxNemg2sIiHjBEyp5AZVyKjBzOdIyZ4s7JulePcelnGWheJWRtl2LUKWWVjQGKCxD1sztSfCDLloOYDrWi4uO9OJfguy9CzRsS0nUNmVaM5pv4whyyhkLEcskynPYDLYBwY7/uPmUexF5xwaa38gHVXslzNHDJRyAQCgeCIYYhCVjKFijLK4lR/buqnfVJkZ+gsyycueKLIS6njXOPirHI5ZJNcY7eoEwpZFfjK6gR56utMXBtpV8olIuRLlm1SmZ696EuWv/D2j6M2BkVtsJHpoNSYRz4t32UZmvp956Hf3+WZ9UR4NMfSdnRSHYxOCgmZSRLLvSIIb51TVpyrkLVGJ62mQiaETCAQHGk88NQ2Mq1w/an1w17KJcPZbU582qb+OvKQuRmXdVshcyVLY5KlOIB7yPpN/WmFzBvraXTSJM9ahIxUPU76Uqb+rqw0oFHIqGRZ2MDc2NuV6rLUUcmSznPnPY/h7M4MudYYZwqqJ/aCFLK8o2SZ7LKcU7Ic59ZDVoQesmnFy7qeuPYda1HwxoA49iJGX1K/m0rAFTKuvq2Qh2x1qKVAIBAsgR/6zQ/ix970kcNexiXF2R2vkJUJU39Z1ygYCeMzLgk0w1EFHrI95pBtzXB8rdEJSC0hhQzwfid6z4+XagLgXi5a+rySJZn6SSEbRbEXVdVWyMKkfurGbAjbw2d3bWxFUzKcF3vhSpakkCWM7rzLsq/E2JVDVkQKWXXACtloDtHzsRe2KzMRI8KHwoejk1aHxqzOlQgEAsESOLtdYGtaHvYyLime3k6XBglVbdxDu6z8oPEqUocyraIuy44csmxgl+XWFNefXA/WVdbeQ0Z+Jwou5eSLiFSY7N9+XXWsEQhN/USEJpFCxj1krmQZeMj8+ara4MK0xNPbBUZ54yFzyla0BG/qtyXLOQqZH53UT8gaJdHYkmVI8middEl9czEXRaiQDSRkVLJkzCRQAyWHTCAQCFYXs7Keq9ysGs4FHrK2+lXWvExZ+yyyqKyZRV2W83PIuu/zblHhwm6J60+tAQiDYblCZphCVqQUsjkly775j2sj7WZZEiEd5SFB4mVXeqkj9YrOR+d8/MIUI61cRlkq9oKUHt9EgOAaW1EQQ3LIMo2L9n9sdMZeGFZ63UduM1rA5xXno7U8eXU4rUByyAQCgWAFEUclHAU8vT3DsUlTGkyqX3U4yzLlISstsYm7LNMesvldlk/ZDLJnWi/fjCl03kPWdCiSYsZ/t6nbP23qH9RlmWfu2l3JMvJ0JZP62ZOUrt+Y8HpHmYa2Jct4wDngiR+piT6HrAq2A75xoJ7TZTnOFLZmRMg6uiwZcexT2xYFD49dVCFrDRc3psmPS+WQrVCXpRAygUBwpDEtq7mltFXD2e0C1xyfAGib+rVqSBARMu4xKqNg2Ez7h7yLc0g81FP5VzGe2GoM/defXAv25eOLamtAp6HU80qWPA6M/GRdpBFoPGQAsFNUjhyMI0KW6rLUHSVLTgjzTLmSpTHt7kiKcojP5z1kjOAoPu8zeSnNMTPt7kFfDhndm/2KvAC89w6Yb7ynz+k3DD1kfHRS+3iikAkEAsGKYFrUc+MYVg1ntwtcc4wIWZjIP841qtqPTiqrvi5LBF2WzZzI9vmGJPU/bcuo156wJUvnbatd+Yt8U5NEydJNHqhD9cevF+4YXYqNK4WWvoOTiEXqGrypn0dS+LW2FDIalJ3oRs1dAG1k6k94yBYZneSujY1OmsYly0R8x16xiPGeyOa0o8syHrAe5JCJh0wgEAhWA9OyPnoK2c4MpzdHTg0D/EN5kmco2XDxqjYsrb9t6idVpa67S5ZDPGREPI5Pwi7LsjIY22HXsamfq2F8f0LKQ9YVzQEg8KbReiaJkuVbPvoofurN9zAi44/h70dI3sa5Dj1kEflx8Rodpv64jDdkdBInRZNR1lLdgHC4+H5W/yYLmPrj2AsdXWs8birIIZMuS4FAILjyUdcGs6odMLrqeHq7wOmNsYtFADyRmcQKWZ3usqzrsMuyb3QSEYM+4kverw1LyFJdlsaSmVSX5WyOh4x3WXZ1E9I6eeL/KA8JWV0bvOWjj+A/vfcBd8wwyNSfj3dk5pqGizel1K7RSXSt9DllhgVdlgMVMp6W3xV7wXPI9rPLcrRAJ2Qr4oOXgLUfN0Xr5/dCFDKBQCBYAcwSkQ+rDmMMzm7PcMoRMu+tAmzJ0pggDiPVZVnayIXMlejQOTqJnp99Chl1Rx6bNGpY2GXJSpZBl2XbQ8a7LLtmWXY9w4k4cJKeMvWTgtaV1E9rbZcs4YlUtAYq63mFDMF96CrjDVXIxtzU31LIDjaHbK5CRve9Qw0EmvtO91Yp5UjZKo1OEkImEAiOLHYLawQ/QoSMcqlOrOfIM+XUL7oHk1yjqkwQDJvykFHsBVWMKM8q9VCnB2hfUj8pW+ujRiHzhIzlkNWIuixTJUvWeJBSyHq6LEl5m9lxQ4A/F+8UndmB3amkfsUJakDIlCNSqdJuPFy87SHjpn5mdJ8Te8GvrWt0Et2y/eyyXCT2Im42CCM+4D7j68uz5t/UfjYiHDaEkAkEgiMLngZ/VOC8UXkWlCyJLI3zrFGBGCFKJfWXZOoPSpbpHDKg2a+P+FKJdHPiOyhpwDcpYpXtCJzkqS7Lfg8ZEbK+LksiDgVTyMZ5qFhRaO6s8gpZmNTf/DUmHOPETf113S4P+qT+AR6yJUz9kzxza2sPF6cuy85DLYxFgmFbo5NUm+CWVfhva6T1SpUrASFkAoHgCGNahOrQUQB1IY4yhZFWLPai+dx1WdaeqCW7LK0xnchN3yxLoHnoVj0BvDOnkPmSZelIYphDNhn1BcN25JCxLst5pv50ydIrZHQ/6N9PmNTPuk6Nwcn1UXP9tmTZqHxt4po7QpYenRTkkCk7gmlADhlhkmso1fjYeMnSsOHi+zrLMsgKG6aQTVOmfqae8e15plbK0A8IIRMIBEcYPrvq6Jj6OdHIM81mWZJyplHWBjNm6q86TP05I2R9OWTAEIWMiIfGONeYVn6CApEUMne7kuUCOWQ1I5VdxMMpZGXtc8ii2IvGQ9Z8tmNL3nFXIF/rjaeboNtx5pP6U8SVlLGu4eJZpML5IeXJSwmuB/Dl2Eyr1lB232V5QB6yeTlkrJQa/zYZI6ZhyVIUMoFAIFgZTMujp5ARYcm1wijzClngIatNMHS8TMReOFO/K1k2akuXaJFnur/LsibypTDONIrSK1GuZElrHPnSIoGrnS4ENlL0gIYodXUT0nmmCYXMKV+1J2vbNgWfkwjiDJSAf8s1xzDKFK7anLDYi/YachcMmy5ZdnVZDo69sGXeXKtLNDppieHiZd3ysWmmkPH7PNJqpUJhASFkAoHgCINUlSPlIau88hN6yDwhK+s62M7nWgJgJS7dSqZfViErmBo2zjVmVeVIISlitMZxlgXfAcKwUzpPykNW9XRZjplCRseL88Gq2hNBagrRiZIljU669vgEd/7gl+Pln3Odi71IlSydQhYHwyaM7hQM29VE4Y/Jc8isQqZ8yXKcaRjry1Nqf5P6lxkuPq3qFqGn36qoTFSy1Cs1NgkQQiYQCI4wjqKHzCtkDSGL/WETO8+RB8bSPvHfTDcPca18wGjXQ31ol2WuG4WMx0rEXZWZbvYLZ1lWrWsMcshqv22eQlZUxpG9SZRD1pDV5rOdWeXWTOBdp2S6v/H0hg3R9c0PsRLkTP3R+VLp9ZlS7F70eMjy0EMGNOXDorT/BjKW8bXP3YrcQzY/qb9bIeNNI60uS1HIBAKBYDVwFLssfSlOIc9USyEbOw8Z77IMlbE4SNTNG5zXZdlj6ueRB6O8KaVS9MbIlSybfZSitbdN/YA39nfnkPV7yGZV1SpZ8tFJ3kOW8ncxU39kus+0cib6mLiSAkeqj/NOdYxO4sfsQpxDRseZMfLrSqj7TMhGeUie+hCXYzm6rvXId1kqpbRS6sRBLUYgEAguJXjJkj+8VxmcaIx0umRpjCcCKQ9ZHPeglbKRFN1ZVvm8kmVtMMqaXClSyOKSJVeF+NqBqGTpFDKmsNhT9/mu/CxL4wzmGbtGuvbZoJJlo5DlEVmrLAGKiccLbjiFb3jh9Xj+DSebfVvBsGEOWXy+FAJCxpoFZqwc60qo+yzPLBIMG0d6BJ91XGtuvYarhLlXo5T6daXUCaXUJoAPA/ioUuqHDn5pAoFAcLBI+Y5WHdyrNcpVi2xRaYvIRiqpn/alB6RWypnYO2Mv5pn6q9o9xMd5hmnp4zZIIaO1atVsC2dZVsGxgHCQuO+y7Bud5L1MReWHmjfrTylkzTmTCpmdZamDz7pjL05ujPBz3/Z5LiYjTtUPVSQkX7evx6tirntTKffvPs+8YrffChn3kM0Lhh2ukPntoyPaZfk8Y8x5AN8E4A8A3Azg7xzoqgQCgeASgDxkwNEpW/p4CYWcqUx8dBIA7HKFjBExnltFD9KmZNk9Oon2LXs8ZEXl1aSxLaW6+ZqRQqZt8n+qyxLw45Nq40uAPBi2ix9wU/+MEUS6RlpDGXnI+PGIP9C1xgrPkJFHtG9zX9Km/ni/vusJDPasTJ1r7b1uB+ghm+e9TxFa95lKX/fI/vtdJQy5mpFSaoSGkL3JGFMAOBr/n0sgEKw0drmqckQI2YyXLHnsRRUSMj5LMsgfM2gZysnU39f1Rz6zLpQ1V8h0EAxLfiTuIeNzOIG4ZOk9ZKQMVZyQDQiGnZW1UwsBni+WUsj8fjxZvvnMH58ImemJ3nDn64u9iHxpXaD7xq8j15qVLP2w8/2eQMQnD8zr3qTAWiBRsuy47pPrY5zaGO3Xci8L5AP2+UUAnwLwQQDvUEo9B8D5g1yUQCAQXAoEClmP4XyV4MNWle2yjD1kWbA/V8jofcrUT7EXXVEE8zxkZWVcCWqca+wWXiGjmAuvkDXr54pbYOpnXZZUduTBsN0lS6+QFZFCRmpMaQeLA4yQBSoYrSHh/bKxF1VP84PbNzL1d/mshpj6+W/Kk/rzTLtS836m9NN5uAdv7v5KoUJbOewy9f/Lb/7clfN9ziVkxpjXAXgd23S/UuorDm5JAoFAcGnAfUdFTzltlRAn9bvRSVHJksC7LIHQUxaWLOfnkPUpZLOqdqRnlGlc2C1bwbDeQ6bs2jtiL6gr1PCh4M1nfX6p3EZTNKOTTDJtvvGQNevYnVFSf3iddD3Ne/+ZUjTRYH6JUEfH4eW5VBNBClQ2pAwywA8mB5r7vFOVvb/bXrBIWTHTCqjaHZldpv5rjk/2Z5GXEToJmVLqB+Z896f3eS0CgUBwScHLXEfFQ+aiJNwsy3aXJUcdKWS8hBmY+ufEJ+Ra98ZelJVXs1pdllFSv1awHrKukiX3i1GXpSeeXaoNlUKpZDkOSn3eQzbE1M/JIyHTvht1SAirVkwhy9JK0RCFbJzwwjWfK1w0/dlse8Eo04MVMkfuWzlk/PVqmfhj9Clkx+3fzwLwhQDeZN9/A4D3HOSiBAKB4FLgSHZZltxDpoMAWKBdsiyZIkb70XseCTEkh2xRD1kcDEtESGuFcd4de1E4D1lzXq3Axin1q0oTSwZjU78nWr77M1WypJfO1B+RNe8tm08ugvJiVw5Zb+yF9ZAxhYwrUC6HrN7fsUmEcaYHe9OIcMbEMCa0q4xOQmaM+XEAUEq9A8DnG2Mu2Pc/BuD3LsnqBAKB4AAxLXyZ68h4yGpesvQKGZ8lycFzyGi/2NTPuyy7Yy9UUFaMUVTegD+2KlXhSpa+XAg0D+lRVLKclbX7nh+d1BBETgbnDuS2RK85XkhegLARxHdZthWyGaXhx4TM3v8h3EIr5YhkPFzcve419ac9ZO6asiZz7iC6LIGGWA+1eXUpZDpBdlcVQ4q7zwAwY+9ndptAIBBc0QhUlSPiIZvR6KQsJDVVXdu8qvDBHytk5IECmEKm4ZSWrlLcXIWM5X55hcz7p5Rqm/ppBBDQeMg2JzTj0nvItFIukBWYTz6oXFpUYclSW3/ZLmsE2e0pWXI1z3/mS5lDSpZdHYapJoKua+F/4+82XZbpyQH7gUVKlv7f0rAuy1XEEEL2awDeo5T6MauOvRvAGw9yUQKBQDAEZVXjZ97yMWxNy6W+fxQ9ZDz9vulUDANTeVfg2ihrdVlygsZVDT9cPH3eVJdlXRu3niCHLNfOWA9Q2dETOqUUxnnmynlA81tuTnJ7jV4NU8oH15oB/i03tikqWdJ17jJVlUqWAenSdJ/SOWSpXLEu0He1is8xrIxH9zMw9XOFzOaQ1T2+ur2g6eQd6iGjSQLh9qHXugroJWSq+Vf7awBeDeBp+9+rjTH/8hKsTSAQCHpx98MX8LNvvRfvvO+Jpb6fGki96nChoK7LMlTIOIEgQtbVZelM/Vb9mpdDFt/j1//xJ/CK1/2xWxeVLEeZRlHWLKJDO7WOzjvOVBB1MS1qHCNCxmZZaptxRcn5tJYukEIWm/rpeztFomSZ6AQkMhkTKa7yzQN9N+5UHDo6iQaaBzlkLQ9ZM1bqYAjZcIWMLrFl6h94rauA3tgLY4xRSv2+MeZzAfz5JVqTQCAQDAKVGbnStQiOYlJ/wXPIbKeiMY0KFudGreUaWyx+AggVMuch4wpZx//Mz1nmGeGBp7fx6ae23XHXRszUX9WBMV4p5YhhU7Jsx15sjNeCa6REfK3gEun5ulNwXZZR7AXQEBiukO32DBcnMhl6yNLdl10YFJbacy3UNco9ZPE8SCpBHwTXmSzkIfOzNjlSkSKriiElyz9XSn3hga9EIBAIFgQRA27OXwRhl+XR8JC5HDKtHeGo6sZHlGkVEIi1cdZK6q/qumXqp3Ji3+DuPOEhK0qDaVk3hJDPsrT5aBenze+6Ps6gFYISZtxlOUuWLBsjOAXXEjno7bK0xy2qujW8WmsVeMhmifIjvUyVJjPNTf3zyQXtk7dIynDVaJzpZHwHQMPF7W9/QB6yofMmO8lnx+ikVcSQpP4vBvC3lFL3A7gIQKERz15woCsTCASCOaAHL/cSLQKudhyV2IuyaoiX1n7gdFE1ClmuVZB3tZZnqI0nQgBcNyUQGrGrGvNLltE9nlU1jKFsL+NUEiIQj5zfBQCc2Ry3PGSjTIcly3JeyZIre933Z9RTssy1cmXK4NoSpMGZ+luzLP39mAdaZ9YTljrvOMcmOY6v+Uc99wg2hKy/O3YviFXMPuSM3HMcJVP/EEL2NQe+CoFAIFgCRAx46XERTMsa66MMO0V1pDxk9PAjw3VhVbCWQmZLiHEKfqtkSV2Wc0z9sUJGRJq6GnkwLAA8em4Xm+MMa6PMRkZ4/1VT1mzeUzYYKWQFU8i0bggcL1n2kQ8iEXw9hEzrwENGSClWRSJvLCRnnUvw5+tQyMKw1P5j/PK334FrT/hUe34syiGjho79xkueew22Z8MabroUstDUv39ruxwxZHTS/QCglLoWwNqBr0ggEAgGgh7QyypkFJWwU1RHykM2ZuZ5oFHNKlu2Cjxko8Z7FHejOkKmUl2WXQqZbilkFFLbDBI3zNTfHOPh87u46tgYQFN6rFi5rzHfN+SIfv/NsZ156WIvrEJGwbX1fEI2zjW2t8tkl2XsIWuuKyIQ9m3ZU85srmdAybKLpCxgdP+cG052rpdyyMyA2ZrL4B98+WcM3jfvuNajZOqfyzeVUt+olLoXwCcB/BGaQeN/MOB7a0qp9yilPqiU+ohS6sft9puVUu9WSt2nlPoNpdTYbp/Y9/fZRf4iuAAAIABJREFUz2/aw3UJBIIjAHpAz5Y19XPf0RHykJGvh/4WlVXIotmD60TICu61SyT1ayI83USjTyGbkkLmYi+a8z5ybgdnNht1h6fcuxyyKlRInUJWe4VMWZJZm2Fdlo2pv/G2pbos4waSriBT5yHrKC8OTeoHEl2We4iCiEcnkXJ42IZ57kdMbY9fryKGCIA/AeBLAHzMGHMzgJcB+LMB35sCeKkx5oUAbgfwcqXUlwD4SQA/Y4z5TDQxGt9p9/9OAE/b7T9j9xMIBIJO0AO6LwG+D9Oixsa4eYgfHYXMKz8j7UcSUWBqWiFjEw16uiz7lJYm9iIkMwUrWZaVcQSRiNDD53ZxZrNRyHjsRZND5v1JTiFzpn7uIbPBtTUrWfY82Cd5o7ylTP1ZwkMWkwTiE0WC/KklS5Zx5+rQ0UkphCVLyiEbptgdJDz57FYDhZABhTHmSQBaKaWNMXcCuGPel0yDLft2ZP8zAF4K4Dft9l8F8E329Svte9jPX6YO+1+IQCC4rEHEYHmFrHJlruLIeMh8nMPIjiQqKx97wR+IE+ch6yhZRl2WdU+eVVeXJQDMqiqYZUklywu7Ja6yhIwTusya+ktbhiSF7FgiGNaVLFmX5bz5j00wbDr2IvaQdUVSUDk2Tur3r4eXLHtzyPaskMF2WS50mH3HkKT+VS9ZDjH1n1VKHQPwDgD/QSn1GJpuy7lQSmUA7gLwmQB+HsDHAZw1xpDL70EAN9jXNwB4AACMMaVS6hyAMwCWS3wUCAQrD1JMls4hK2tsTI6iQhY+6IuqRmW7HFMK2SwmZKnRSXUzlqhzdFKW7rIEqGTpCRAPMj1zrClZKm7q197/Nqtqp+D5kqX1kNmh2S6WY0CX5TjXzlMYlyw185BR52bMh1wOWTTNgL4T79cH59FbMocseczou2aO9+9SweWQtUqW/PVqE7IhCtkrAWwD+H4Ab0ZDqr5hyMGNMZUx5nYANwL4IgC3LblOB6XUdyml3qeUet/jjz++18MJBIIrGHtXyGpvBD8iHrKyrlvm+aKyGWI6LFmuJ0qWyWBYTQrUgl2WzNTPuz85EeIlSx57QaStqGpHyL2pn49OaiI+jAm/34VRprFtR3GlFDI614Y9V5epn8hmOBx7MSJFu8dlPL78RYkU/804UT3sjK/OLksx9Qf4VgCfYYwpjTG/aox5nS1hDoYx5iyAOwF8KYBTSilS5m4E8JB9/RCAZwGA/fwkgNZ5jDG/ZIy5wxhzxzXXXLPIMgQCwYqh2oNCVloj++YRU8hmJStZUpeljbLIO2MvuELGYi/crEVfspzXZWlYdHvBFLLGQxauCwDOHCNCxmMvlFfISk/I1qMuyyYXDb7L0oTrTmGca1y0PrGUqZ9A5dG2h4zKwIlg2ICcdS6hdb7+zsP5x+Hg80e1bXag+3SYcDlk+6gGXmkYQsieDeAXlVKfVEr9F6XUa5RSt8/7klLqGqXUKft6HcBXAbgbDTH763a3bwfw2/b1m+x72M/fZvj/5QoEAkGEvShkLVXliBCyxqtFXZZeZUqNTkp2WVZphcyXvtLnvf5kk5r0zvv8/87mpv6ibueQAXAeMh4MSzlkzTGMU/DWRlnjAau9QqYt8ahMe90p8HOPWzlk/v1mByGLRydxkY3vuliX5X6WLJsFac0iOur60MmObxAJty+a3XYlYy4hM8b8M2PMSwE8D8AfA/ghNL6weXgmgDuVUh8C8F4AbzHG/C6AHwbwA0qp+9B4xN5g938DgDN2+w8AeO2iFyMQCI4W9uIhIy9QPG5n1RF2WfqSpR+d5B8LvMuSnot1gtiQab6uuz1kf/Xzb8ANp9bxr958t8sDo0aK3aKCMV4ZC0uW5CHzZeUuhWyca+Rat3LItIKd2dgcs688x88dlyyThKxjGLYfq+SPscjII75PX1jq4iVLv076Ls38PExQh20WNTDEw9lXGXNN/UqpHwXwZQCOAXg/gB9EQ8x6YYz5EIDPS2z/BBo/Wbx9F8DfmL9kgUAgaFC54eKLx144hcyVLI+Gh6wZUdQ82Ea5D4YllYSP6ZmQqb+qMck1dotGSYtN/c3g7/7RSZM8ww9+zXPx/b/xQfzOh/4Sr7z9BvcbXLRp7vRQ7ipZ8nFEpKbNqtopeJO8mZ3Ik/pplmVlhpUs+blTo5MIpKzGJMHFXiRyyBZO6h/QZbm8QqbY3M3DJ2SefIbbg2sVDxm+GY2S9YcA/iuA3zbGPHygqxIIBIIBKPehZLlxxEqWReUDT3OnkNWoa7Q9ZHa/aVFjYsNakzlkLOerr4PxlS+8Ac88uYa3fPRRd14A2LJDxCkXjXdZXpXIIdMKkam/st/LbByGzyEj8zoPhu3jMJyQDVLIOtQrF2LbUbI8rC5LnoivAoVsocPsO7qS+vn9O+yy6kFjSMny8wF8JYD3oPGB/YVS6k8OemECgUAwD3sx9cdRCUfF1F9yhSzwkNVtD9nYj06aMDWtNTqJKVB9RENrhdMbY+xaRYsIGXU1xsGwNMcSiDxkOixZEiGf5Bq5VqzLEm64eM2I5H6ULI91lCyd6pTwqy0ae0FkJM/icyxfsnTjmFjJsrgsPGRWuesoAac+WzUMKVl+DoAXA/if0ATCPoABJUuBQCA4aOxJIaNxO2ManXQ0CFngIXNdliY9XDz3HrIT6564poJhyaM1L897MtKODM9cybJ5H3dZUgYZYD1krmQZkkki5JORtsPBual/wS5LXrJtdVn6992xF5bk2DXx+7lo7EVnl+U+KGS8ZFn1eP8uFTpnWR6hLsshwbD/Cg0Bex2A9xpjioNdkkAgEAyDV8iW95BtTMKohFUHJ2TBLEs7Okk7kuU9ZtPSjxFKdSs2syL7c8gIk1xjWtaoa+NI8EXK/dKhQkblymY9XiGj0UkABcNaQpZlyDPlSpZEEJWCVfAQrDuFPoUs8JDN6bIsEjlkXOgawn/ou+0uS/Z62RwyppCVlTl0fxZ5F2MVLBgTNcRkdQVjLiEzxny9ja14tpAxgUBwOcHFXixBplzJ8sgpZMYZ4v0sS9M8lJlClmfavTYGzkNWcqUp7rLsMfUTJnmGszuFS9MHGCGLuiyvPhYSsu4cMushG4UlSyKImVaYlfWgkmXoIUuTrXGmHUFtEYiekqVeUO0ZFJa6IEnJGeHmDQiLHme/0amQHaGS5dyfQCn1DQA+gCalH0qp25VSbzrohQkEAsE8uNiLYvmS5fooaxSUI0LIyool9btZljYYNvMeslHkJ6O5lpXNLAPCLsu6Rm8OmTtOrjEtqmB2qC9ZesIDhAqZYqb+LEjq97MsxxmVLMPYC1LwiEj2rZErZF1dlqPME8LuHLK2Qrao9ytzClm3l23Z0Ul8KkN5GcReHETm2pWGIZz4x9DEVJwFAGPMBwDcfIBrEggEgkGgqIrlFDLmO7Ip8kcBMzYzMphlackLbRvlOng4OlM/N8ezqIKqboZ3z8uKmoyywIgPoDWqaJxpKBV6yMKSJQKFbLesMM41tFa2ZNnsR/laWilUrMtyaOxFy9RPymKuHVmLrzcuWXJDfjhGqXMJfv8BCtnSJUutgrUeOiFTnihyHKXRSUM8ZIUx5lxk+Dsa/59LIBBc1thb7AVFJTQDtY+Kh4wn9QezLO3oJHoe5loHD8dU7AUvM/HQ1j6Qh6xg93uLuiyZevOv/+bt+ILnnHb7xB2KI+Z/251VbqpArr1CZlgOWV0bF0g7tMuyNTpJ0X3TregQgrJfIQUwzAxrH6sPXiHrVo0WDUsNRicxU/9hq09EdtvDxY+OQjaEkH1EKfUqAJlS6lYA3wPgXQe7LIFAIJiPqtpL7EXznbVR1viOjohCVpSpLsvaesg0lGp8ZOMs7LgkApIy9WulHCkeZuqvQoXMliy5IvXK228IvhdkeGkEpv6dwhOyUaaC4eKU1E+zNvm6UwhHJ3WULLVvKuhK6nem/o4uyyFEalCX5cIKmVf2/NxNM0ixO0h0zbLkb1edkA0pWb4GwPMBTAH8OoBzAL7vIBclEAgEQ8BLU4sqXE9vzwA03XJZpi5LD9luUeGP7318X49ZWK8YwLssG3+VG6ujFfJMhx4yImSVn1lJD/SMEdq5sRd5hmkRKmRxUn8KKipdjVnJcqeoXWZarnXQZamVjeUwfsJAn4oXdFl2DBcf5RoTMvVHT9HWLMs9eMiInMT3hX91P0YnlXV9+F2WOk1wFVPyVr1k2UvIlFIZgN8zxvwTY8wX2v9+1I45EggEgkMFmbSBxX1k9zx8ATecWsexSW4f4pcfIfu9Dz2Mv/OG9+CRc/v3/3KLykdYjJiHrBku7stwoyyca8m7LMuoxBV3QPahySGrg98r7rJMIVDIVJhDtsNLlsHopMjUP2C4+KhPIWOjnZxCFpcs7VsihXwUVdeg8S7QV3s7DxfsjkyNThrSHXvQ8OXv9mddSuGqofenNMZUAGql1MlLtB6BQCAYDE6iFvWR3fPIeXz2M48DaB4G1WU4XJxUvK3p8olDH3rwLJ662ByHjPejzD+UMxsTUdcm8ISNIoVsZDswK+vFitUeHtrah0mug/mTALBNo5N6FLLQEM9yyMoau0XlFLJwdJJVWOy6h41OYmXaiB3QGka2mzNeF39P/x67FLIh5MI1Teyjr4oTnzA+47AVMlpXm5Z03YdVwxBuvYVmXNIblFKvo/8OemECgUAwD5xELeIjm5YVPv74Rdx23QkAzcOguAyHi1/YbZSj3SViPQivev278cZ3fhJAuvMv14opZN2ELNMKmVXCSkbems/g/FlDcsgAr4oBrGTZI/fEnYWBQlZwU3/sIWv2rw0GlSwnQcky3I+ueZypTlO/yyGr2mrcsiXLmKTojmMOAc+OC0ufCx1m39GnkNE1rjgfG2Tq/6/2P4FAILissKxCdu+jW6hqg9tIITtkD9lH//I8nnlyDadZ7hbgScsykwiA5p5sTUtcsMchQsaVHxo1VAWETCOPTP259qW/qjYhKVig648Iz/ldT8jo1vd7yPxr3mU5qwx2ZhVOb4zZ9YQ5ZGTqN2ZvJUsiRn0lS5/t1Z9DNoRcuC7L6L5kCyptqfXxHLJljrPf4A0iXZ8d9hoPGkOS+n/1UixEIBAIFkXFVK1FSMs9j1wAgEAhO0wP2d9+w7vxrV/4LPzjl98WbKc4iGWCbwFP6IiskmrDidbIjhrihCxPKmQ+Bb82kUIWkI7+NVHALI+6oHvf7yELyYyyxv64ZMl/y9o0HitfsoTbpwtEtCgug4OWN+pJ6qfmg9S9DmIvhpQsD6TL0itkl1PGVx/pok2rTshWfDKUQCBYZXAStUjJ8p6Hz2OSa9x89SaAxtx+mB6yC7uFK08G251Cthwh24qUMfJ58e7B3CpKVatkGSlkmbLdqHXL1N9VlkuBSpZbu40v7tia1wV6CRk3xFO3Y6ZYydLP5+Sjk5RqSq1mYJcljwSJO0YzFprbpZA1x2+vFWh3is4D3Y52WXR57xePl9hLt+Z+o2t0Et922Gs8aAghEwgEVyyqJUuWdz9yHp913fGAgByWQlbXBkVlkusnhWu3WK5kSd4sUmuos3HE2M1IN12JZV2zOZaNQhbPXiQ1K2Xq96/710QlSyKLxyaekMXEgyN1jnGuWx6ykeYlS7hZlhXrsuxbIxGtSYIc8hyySUdSP1/rXucyZl3H2UMJz6lukUJ22IO7XffnES5ZCiETCARXLKolFDJjDO5++AJuu+6425ZnPmn+UqPoGf+0tbs3heyi7V4kskfKETerjyyp4dEHDfnSLQ8ZjS9KxV4Q5ueQNY8dUgQ5IetTyFLq0siWLHdmFdbGPvbClywb4qiUQlWjFWibgosEydtr8cqc77JMkcguQhaofAO4hcsh62gcWKbrkCtRl1PJMu+4VuDodFl2esiUUr+DnhFJxphvPJAVCQQCwUAso5Cd3ynx1MUZbr3WEzKKczgM0LqThKxHIXvgqW1MRhrXHl/rPLbzkFXkIbNdlowZ5Fo5jxo9DK8/uY4bTq21ypK5vU+zsg66EcN8rTmEzCpZVI7d5ApZb+xF+xyjrMk0m5Y1S+pnCllNOWRmcDCsL1m293EKGS9ZJo6lNYAqHXJKGKL2eIUsJIcUlrpoBhk/L88ho/eHCb6u7s8u6ZIuOfpM/f+3/fvNAK4D8O/t+28D8OhBLkogEAiGgJSaqjaDVaSzO00mF+9o5FEJC6+hqnHPIxfwOTcsF9foyomJ9W/1eMi+5z+9H8++agM/+62f13nsLlN/PEB71zZEUIjpG77jDpst5u9JrpWbaLBTVFizBAhAVNrsu1pWskwpZANjL+h0k1zj/E7jRUvFXrhZllbZG9Jlmdnuw3iOJf/eiMVeHGjJskc1ohiSRZG7RPxYdVz4UPsKIuNJgttxP1cNnf/6jTF/ZIz5IwBfZoz5m8aY37H/vQrAiy/dEgUCgSCNqjbYsA/ioQoZlcpOrHHvkp6rkH3yiYs4v9sOaP3Dux/FN/ybP8Gj55dL03cKWS8haytkT1+c4ex2f2BsbOqnv1z9GWUaO3aWJD0MJ3nW0WXZTDTgXY38e8DwkqXzkK0NVcjSJUv6TdzopIyPTrKxF5riOuha+tc4ZiVJDp9D5rss+whEfJpFzfhdXZZ0rGVULT4ei3/9sMuBfaRLTP0em0qpW+iNUupmAJsHtySBQCAYhrKusTFpHsRDYy9IUTmxPnLbhnjIXvX6P8PP/uG9re1ntwsYg2SX5BDEZIlgjHEqUioYdjeaB5nCxbjLsiZCxrsslVPg+uIVKIesIkLGFLJluiwvUJfleGCXZaJkOc41zu8017jGh4uz2AtSkuralyznPddHmWplkAFcIdO9ChkdP4+T/hcenWQVsgRRJSVvUXDzPP/+PCJ90OgaLg4cHVP/kGDY7wfwdqXUJwAoAM8B8N0HuiqBQCAYgKo22BjnAKbDPWSWCBxnysyQLstzOwXufWyrtb2LUA3FtEMhm5a1W1OKbO4U1dxrvjgLTf2z0mZjsQf8ibURPvTgWQAJEzpXUDQl9TddjSfWRmy/4aUvyiFzpv4g9mKYQqZcHpjC4xfikqWGMc2/jdoYKGVzyPgsyznkY8w8YhyckE06kvr5fl1jlVKfpTBXIdtHU/9hkx2ejxZjL00MVxKGBMO+WSl1KwBKLLzHGDM92GUJBALBfJS1cQ/ioR6y865kyRSyAR6ysjZ48Knt1nbyZS3rQSMiF5v6t9hoobRCVs0lgVvO1G/XWLeT+r/jy27Cq3/lvQDa5EIpH3WRu1mWCLoagbR61YW4ZEmm/kyrXpUmVJcSJUs2XBxo7quxnaMGQD2wyxKYX7Ic5X50UxdZaj4Lty+iJAJMIUueY7kSHjfPX1ajk7L0PWu2datnq4S5JUul1AaAHwLwj4wxHwTwbKXU1x/4ygQCgWAO6tpgY7yYhyxVshzSZVlWNR58esepLASnkC0Zm9HlIdtiJdBYITPGuO7CPmw7U38VrJWX0r78udfgS285A6DdzddsI1Kg7YipGrtF3VmynMcRfMmyRKZVYMbvQ9ChyEqW21YF9MPFm89Kq5Bp1TzkK9O8B+Y/2Ee5TpYsXeyF1q5kmCJFXYqOXpAAeb9X+ndZJjuMq3eX13BxHfzliMurq4ohP+evAJgB+FL7/iEA/+eBrUggEAgGoqwNNqzCkoqNSOF8oruPG8FTqGuD2jTnePRCaN73KfiXTiGblsPKpFtTImLUyUldlqF36EdecRsyrXDV5qh1DN7pR6XdnchDFgfI9oGPTuLdin3+MSA9B5KTpjVWsgQaAl1ZU38zXNwTsrkly0wnc8icQmbPe92JNVx9fNzaj8hjFpVgF4296Moho+8vU8LzpcHFS6gHib7h4ste65WGIR6yzzDG/E2l1LcBgDFmWx22+08gEAjQlKDW7INzOjDN/vxOgeOTvGVY71PIuL/sgad28MyT6+69L1nus0I25QpZ+Bl1Rc4joXHsRcrUDwAvuPEU/vS1L8XVxyatY3BDNd2nnVl3l+XQkmVl1U0iZH0dls1x+euQGAFgOWRUsjQ29qLpJjTG/47zyNC1Jya49njqXlBobPP93/ueF1kPY3qtMYlYpBuV77+fXZbcPH85dVnOGy6+6hlkwDBCNlNKrcOGxCqlPgOAeMgEAsGho6yN63ibDiREF3bLoFwJNP8Pv+hRuKqAkG3ji26+yr33JcvlFLI4tJUQlCwjskm5YUXZf04/OimOvWg/3a49kQ6Y5eOUKJtsp6gcEQZChWweR+Cq1iTXbkTRIgoZnYMb73nsBeDvK/dakYo5j3v8wt/+giRB4bEXAHBqo62OAZ7cxIRpURM97d+pkC3VZenN8yqhOh4WzthcwNT/KFBLNjBcaRhCyH4MwJsBPEsp9R8AfBmAVx/kogQCgWAIaCD2JNcubX4ezu8WQYclMF8h4/6wT0fG/nhw96LoUsiITJ1Yy7EbfUYlzHkK2VYrqd92WS7wIPfeHoU8UzhnPXhrS+aQKWV/r7JuuhVtCXM010PmX6cUMvIS0rXR/eT+I/qt5qlBvOGDg49O6l9rumNw2dFJXQrZnkqWsUJ2yB6tW59xHO967Utx/an11meZOnwF71JgSJflf1dK3QXgS9DEXnyvMeaJA1+ZQCAQzEFZ146QDfaQ7RStB27jIetRyJh69sDTMSEzwd9F0ZXUT7EQZ45NWgoZlSyLebEXraR+22WZ8Ed1gc8YzLR28zFDD5nff4iSwQkZqU1xZleMpIcsb3vIiCx5Qua/S/d6WfIRe8g618rCV4Ptiyb12126csiWC4b16t2iXZ8HjRQZA5a/1isNQ7os32qMedIY83vGmN81xjyhlHrrpVicQCAQ9KGqSCHLBitkTckypZB1f58rZA8+tRN+FoWuLopZFRrvCUSmzmyO2wqZLVnOK9NenIbdleVSChkpKs2wcSKKASFbcATPhPm9FvWQaeUVqDH7Thx7QYRMKd+NWFY1lFo+BJWPTupfa1rZWjj2gt371Gd7G50UliwvZ8Jz5LsslVJrSqmrAFytlDqtlLrK/ncTgBsu1QIFAoGgC5UxyHXzUB/eZdlWyLI5OWRUzlSqr2S5pIeMDRenWYtAU27UqvEptTxkhSda/DsxqOxZm4aMOA/ZAgoZ77LUSmFrGo4q4vsAwxUyABjn3tTfN8eSH5cfn2eBEUkiwkFEl8c7FFW9p9IXj9sYsta+oN2FSpYd0RpLKWRsbYuu57DQFS+yaugrWX43gO8DcD2Au9CUKwHgPIB/c8DrEggEgrngHrLZAqOTUh6yvpIlka0bTq3jobM7mJaVy9PyJcvFFLJHz+/iGSfWXGgr0JAyntO1OcmxNtLtFH+rBlIifZe6dHFauoy1ojJ+uPgCLWs82T3XyvnX+HDxbEGlxRGyTLnrnaeQqQQhI2K0Psrc50TMpomS5awye3qwZxHp64JX8/Zm6u/rslw6hyzz9zFYz2VMeJa91isNfcPFf9YYczOAHzTG3GKMudn+90JjjBAygUBw6Chrr5ANSeqva4ML01SXZf9wcSJrN1+9CWOAB5/2ZUtfshyukN376AV88f/1VvzFg+cCssXLlhenJY5PcqyNMqeIEXbY+y5lcFpWKCqDU/ZaZ1whm0N+OIIcskSJEFisyxLw4bB8JuQ8XxZ9zHkDfYeTw7zlIQtN/XuJT/AesmEly7g0HPrg5t8ol0OWON+ypn6e93U55ZD1YdlrvdIw5J9mrZQ6RW9s+fIfHuCaBAKBYBAaD1ljDB+S1H9xVsKYdhfdaM5wceqgvMGajp/cmrnPlumyfOxCkxz0yPndQFnj17A1LXFsLXcGeA5O0LqiL8g/dtrGCczK2q1xET9OnENGWDaHDPDhsNzUP5TkJBWysX+UUbem95B5YlPuU8lyXsnXrbWDkA29//MUsr2a+oPO1cu4Zimmfo+/b4w5S2+MMU8D+PsHtySBQCAYBpqxOBkNU8jcHMvI1J9phdqgNRaJnwcATlq1ibxZwHJdlkQWdqMB4TEh2+xQyHhy/7RKl2qpKeD0xsiur8asMhhneiFTe8ZUGk4Muroshxzae8i8Qja/DEiEzG8jMreeUsgqppCRh6w2e3qwn7T38nRH/hiB7kFX7MXQJXB1MsbSsRcskmPRZozDwpE39TNkPJlfKZUB6P/XKBAIBJcAVd14goYqZDTH8ngce0EKShchs2SLSp0Xp5yQLd5lSeRxJxoQzl9vTUscmwxQyKIyJ90HIo0UXkoK2TyvVgxf4tIBAVjr7LIc4iHzJcvJwC5LOiwnVKSqcULmPGQF95A1nxVlvacH+/OvP4k3f9+LcfuzTvXu123qb94PJcS9OWRLGt21Vji+luPE+igg0pcz4cn05e1x2y8MIWRvBvAbSqmXKaVeBuA/2m0CgUBwqCjrGjnFXgww9VNkQ7vL0o/z6ToP4BWy7SknRGHo6hCQejONFDJOvLZ2SxxfyzHJM5S1CUqigYeMfedbfvFP8TN/+DEAaYWsqOq5Xq0YXKXhpGl/uiyVez00qT/osszbHjJS3Jypn5W7iqres1fqtutOzN0n6yBSPCV/CE7Y5pNUUG2msLQf7nf+0Yvwd7/0OQuPcjosnFwf4fh6Oqx3lTAkqf+H0XRc/gP7/i0AfvnAViQQCAQDYEwz8Duj2IsFFLJUDhlAxCuLv9YqWW5N2yXLRWIvfMmyDkz5rZLluOmyBBqCQeW4adEmhADw8LldPHJu137fesisQjYtaxS1WcjQD/hSYlyy5KOT+IN9CN/jIa7e1D/PQxb+BVjJkpFD2kYqouIlyz12WQ4FrTEmZCpxDX14ya3X4Pe+50V41lUbrc++/gXXL/xbEm66etOu58rosnzt13528D9CVhVDkvprpdQbAbzNGPM/Dn5JAoFAMB+kZi3SZXl+t6NkaR9snQpZFXnI9liy7PKQtUqW1tQPNIRq04752+3wnXHFzSnjhwnjAAAgAElEQVRk1tRfVDWKcnGFjFSYXKvA58VJEC8jDlFafOwF85AtoZDx2It4G4XnaoWgy/JSxCekIjqA7hmXXdBa4fnXn0x+9j+/6OY9rNAeX6VfX264avNouKSGJPV/I4APwJYplVK3K6XedNALEwgEgj6QapVllEM2nxD5kmWXQtZfslwfZxhnGhdnbQ/XIiVLIl6xh4zUMmMMtljsBRD6xnY7Yi+mZe2I6VarZGlcE8QicMnuWkcK2R5KltRlmbMuyzmMQCX8VylTvytZOg+ZL1mWtbkkSpBXyOLtaaJ2WAi8f5czIzsiGPK/Ff4ZgC8CcBYAjDEfALB3ai4QCAR7AFfI1kbZoJJGl6mfPGRdZceSnWtzkqUVsgViL4KSZULt2i1qGAOsj3NHXrgCuDNre8jKqkZZG0fQaI3c1D/bo4eMXk9y3Zk9tkgO2TjTyDNtB5cPDVv121wO2ThrbeMKmTP1V/UlIR4+hyy8pj6T/mHgSskhOyoY8n+ZhTHmXLRt7v8UVEo9Syl1p1Lqo0qpjyilvtduv0op9Ral1L3272m7XSmlXqeUuk8p9SGl1OcvfjkCgeCowClkWuO6E2vYnlU4ZwlXF87vFlgb6dbom9BDljiXmwGpsTHOo9iLxYNhiTTt2vDWeDuRy41x5shLoJAlypz03SIqWV4VlywXdILzpH4iErxcCSzTZRn6xsaZXiqHbNRTsuQKGalisz12WQ4FEa92Dln497DBf6qjkIR/uWPIT/ARpdSr0MRf3KqU+v/Ze/MwOa763P89tfQ+u2a0b5ZkS5YtL8g2xmaznWCzmcUBDCHEIXESSH6Xm9wQljwhueGGJNwkhIQQyIVg54EQEjAQbAixcTBesC3jVZYlS9a+jGZGM9P7UlXn98dZ6lT1PjM93SOdz/PomZnq7urTNS31q/f7Pe/3bwE83MLjHAC/Sym9EMDLAXyQEHIhgI8AuI9SugXAffxnALgJwBb+53YAn2/vpWg0mnMJ4ZCZBFgzxAJbj03nGz0E6YJTc8dasx4yMXjcMglSUSvgkDlzGJ1UUnrISjUcMiHI4rYZaOoXFGvsslRnYgJAruzCNgmSEUs+vlBxkYhWb1poRGCXpRBktlnzPkCrOWTcIePiaSBuV7mWYWo1ytcsWYqND44yy5I/ZipXXtymfhI+3mMlyzZLzZrO0oog+20A2wGUwCIv0mAzLhtCKT1JKf0Z/z4DYA/YUPKbAdzB73YHgLfw728GcCdl/BTAICFkZRuvRaPRnEMIN8s0DawZYrvQ1JFGtciUKlVjkwD/Q76ey1Vx/ZJlImrKFHzAF0Bz22XJesiS3HGSgow7cHHFISuFesiEUBNCsBQSZjkeLBux/Ib2fNmVAq1VzFoOmT1Ph0xJ6geAO37lSvzGqzc1fIzfQ+YfE6+t9i5LEXsB+ZonMiW8/LzhpuubL/WS+mVKfo+In2CpuTfWdC7Tyi7LPICP8z9zghCyAcBlAB4FsJxSepLfdArAcv79agBHlYcd48dOQqPRaEKI6qJlEMUhayzIsiUXyWj1P3tWkxwy2a9mGkhFrVDsBS8VzmmXJeshS0Yt5Mp+g3+hzDcRKA5ZMeSQ9cVsFCslOZxclOjEufNlF3HbRMQUQ9A95EoORtrcseY7ZH4wbKyBQ9ZKSTCcPXbBir6mj6lZsqzhkBmGGILuO2RXbxrBP/3yFdi+qh9j/bGmzzVf6s2ybDf2otME40p6ZFHnMHUFGSHkP9CgV4xS+uZWnoAQkgLwTQAfopSm1R0ylFJKCGn9v5XsfLeDlTSxbt26dh6q0WjOIqRDZhAMJmwkI2bTkmWx4gbyswRqLEItxHHLYCXA8XTRv82Zg0PGxx0JhywVtXA6U6ouWdZ1yDz0xyxMKI8RJTrZU+Z4iNkmbMvvn8qXXSQi7ZUsZQ+Z6Q8XD/eQBXdZNj+n2tTfKuKugRwyOcsyuB7bNII5ZAbBa7eOtfxc80WOTgo7ZG3GXnQa9fNYG2Tdp5FD9n/ne3JCiA0mxr5KKf0WPzxOCFlJKT3JS5Kn+fHjANYqD1/DjwWglH4RwBcBYOfOnW2JOY1Gc/ag7rIkhGDNUKKpQ1ZyPJklpiJER3OHrLpkWeG3zSWHrFBx4XpUunbhpv5YA4dMlF7DJcuSItBYs7xf2syXHSRqOISNEDtQG/WQqfqirRyyJkO6VWple4lSZLgvMGIZfsmyC0KjXmmy53rI1Kb+HlnTuUzdv5mU0h/P58R8/uWXAOyhlP6VctN3AbwPwJ/xr99Rjv8WIeTrAK4CMKuUNjUajSaAv8uSfZCsGYrj6JnGDlmp4iLWF606Lpr66/aQKc+Vivq7LCmlcxudFIi3oBjkWWF+D5nf1F/TIXNcrIrFA48RDllZEWhRZUdpyfGQK7myX61VhDPFesiqRxUB8+8hawWjRg/Z2uEEvvqrV+GKDcG+MBYU7AYet5jUnWXZy7EXPbKmc5lGJctnUbtkScCqjTuanPsaAO8F8Cwh5Cl+7GNgQuwbhJD3AzgM4B38tnsAvB7AfgB5ALe1+iI0Gs25h++QsQ/1NUNxPHbwDCildV0a1gxfLUia9pBxkWMbBuv34j1krkdB+UPayiFTZlmCAKmoHTheDJQsqx2yQtmrdshCPWSlioeoErwqd1m23dTPg2GJ4pAtUMmyndE/9cYRXbN5WdV9I2Z3HbJ6axW39YoZpXPIeotGfzPfOJ8TU0ofBBNvtbi+xv0pgA/O5zk1Gs25g+jZEibLmqEEMiUH6YKDgUTtCIWS40mBoyJ3WTYJhjVNgmTERMWlKDsePEqr7tMK6i5L02Rhs+rxfFnJIbOrHbJSxZXTBmSJUjp1fvxF3DalCyVCcZNtxl5YBmHhqsqQ7nAf3lxzyNopWbZT7lMdsm4MzRbPWasMqOaidRuiXH5tkHWfun8bKKWHxR8ARQAX8z8Ffkyj0Wi6hqsEwwJ+FtnRBo39dR0yWbKsEwzLn0s4ZACLlVDHFs0lh6zAZ0/GLBOmQQIjlQBWGlRnWcrX4bgyt6uuQ+a4iFiGjKuYzpcBYA4OmZ+i35JD1sYuy3aa+oWGaUVg2SZRHLLFVxpmnZIlwIVtjwgydR29UkY9l2llluU7ADwG4BfAyouPEkJu6fTCNBqNphEu9Zv6AbSURdbUIas7XNzf0SkFWdmROyzZfebWQ1ZxKSK8tKg6Z4DoIRPJ865cS8WliNsmLINU9ZA5HoXnUVmyBJhAmcnPzSGLR0zpiNXLIWu3ZCmuYTsbDHyHrPl9gz1kLT/FgiGGIdQSp71VslS/75FFncO08rfh4wCuoJSeBgBCyCiAewH8eycXptFoNI1wldgLoHlaP6W0rkMmxgm5XFT9eN8Exvqi2LayH0BolmVEOGRuYFZhZY6jk0yDwDZZ873a1M+Osx2kUcvw0/0dMeic7aAM77IU5y+7XsCJEoKsXYfstms24DoeGSHEb/gakjY/2Lev6sc//OLLcM2mkZbX4Y8daqFkaRqB0UmLjXDxwjlkAHPPesWN0j1kvUUrfrEhxBhnqsXHaTQaTcdw3KBD5meR1XbIKi6FRyFjJFTCDtlHv/kMPv/fBwLPJfqohMOULTmBMuVchotTypy2iMXElQh5LVRYqKv4YI9afq5WUSlnqiIuLMhKFU/2aEUsAzMFVrJsN6l/rC8mdzHWm2Wp9kS18rlOCMGNF61oOlBcRc6HbOH8tmnI4eLd0BmN+t0M0pslyzZHnGo6QCu/gh8QQv6TEPLLhJBfBnA3gO93dlkajaYXef5EGlf8n3sxkSl1eylKD5mIQyAYSkZk83oYUcISO/xU1B4yz6M4nSkhrwwQdzwqxYMot+XLYUHWfskSYKIsYjIXTM0nU12omG0G5l8CQMwyAyJObfovOx5KjhsIYJUOWZslS5XWSpadERsyh6wFRRaxDBlD0p3YC/a1bg9Zzzhk/ve9stHgXKaV0Um/Rwh5G4Br+aEvUkrv6uyyNBpNL/LCqTQmMiWcnC1gtEae12LiKGGtglTUQrro1Ly/aPJu5JC5HsV0vgzHo3KnI8DcL+HE+SVLR/nQb290UngDQMQyuIjwS5Zqon7UNqoFGY/EqOmQOV6gX862DEzOsOkC7Sb1q9QNhl0EQdZOyVLtE+y5pn7SOzsajYCz2SOLOodplEO2GWzu5EM8Zf9b/Pi1hJBNlNID9R6r0WjOTjJc7LSzo7BTCIdM/VDpj9nIltp3yIRIyZYcTGSZ+1dQHCfHo1KMpLhDli35syfjttm2Q2YaRL4G2zRgmyTQQ6aKnphlKiVLLiwt9piaPWQOm5GpBrCKvrV2S5YqMhi2QcmyU2KjnaZ+NXC2G+KHNBBkptFDsReqQ9YrKvEcplHJ8jMA0jWOz/LbNBrNOUamyMROO6n0ncIJBcMCQF/MkqIxjBAy0RoO2WgqCoMAJ2eKshxbUB0yz5MlS1HyU0uW8YjVdjCsOsJJOGTq6KRYM4cs1EOmlkELFReOR+VgcTVeohMOWbuxF3NBnLYVJ0fNN+uG8yNLljWem/RQDxkhpOcGnp/LNBJkyymlz4YP8mMbOrYijUbTswix044b1CnCPWQAkGooyHwhE8YyDazoj+HETAGn09UOmVvTIfNLlvGI0dYuy5LjyWBXgDk6EWXHJGvq9/95rumQ8dBX3yHz1ysmCUiHTBEoyTZnWaoYdQRZu7ss5wJZQg6Z0aDfrZdiL4Dem695LtNIkA02uC2+0AvRaDS9T7oHS5ZqD1lfzEK2VFuQCYepVg4ZAKwajOPEbEGWLNUesorrC7IoD1vNKyXLhN2mQ+b4o48AyF2Wqgumip54xJQiq6BklKmumlqyFKJUvNYoFygGqf/6W2HjSBKrBmLYOJoMHF+MkqXZhnBQHbJuNNAL07bWZKheir0AlN68HlrTuUqjv5m7CCG/Fj5ICPlVAE90bkkajaZX8UuW3RdkTiiHDGAzITPFCiitdqtKDRwygAuymaJ0yIrlkEPGRQ0hBImIGYi9iEda7yGjlKLseuiPKYKsRg6ZGi2xLBXFZJbFVvhOH4/KUGZXCtL89xSRTf3+hoT5lPDWjSTw8Eevx+rB4P/JF2OXpRQ5reyy7BGHzKwR69FLJUug8ZgnzeLSyLv+EIC7CCHvgS/AdgKIAHhrpxem0Wh6D+E+1eoh+9T394BS4GOv37Yoa3G9YA4ZwByyiktRcrwq4dXMIVs5GMP3nytgPMN2I6oly4qyyxJgZUt1l2XcNlveZenwgeThHrKoZeCM68+yVNe/LBXBVK4kw20Bf6yScM7UkqX4PflDvIP9bwuN6EWitHPlOCFilkYPWX2RY/ZQ7AWgOo9dXoimviCjlI4DeAUh5LUALuKH76aU/mhRVqbRaHoO2UNWQ3zsOjS9qM6ZU6OHTPRlZYpOlSBr1EMGAKsH46i4FHtOpuX5yw4LV2UOmf88iYiJfFkpWdZxyP7wO89hOBnBh244Xx4TjlZfqIfMDo1OUpvvR1JRFCsecmVXJvWLHrJaJctsqGQpHKP57LBshkkIHEp7oodMdci64fz4OWS1b+sl8aNLlr1DKzlk9wO4fxHWotFoehxRslR39AnKjodcuXb/Vieo19QPsHWGc9JEcnvdkuUAK8O9NJGTxwoVV4aMmspuzlTUqi5ZehSU0oAj8+D+SYyF1iGuXbiHLLzLMm4HS5YAMJUtyVIqK1kSVBweDOt4iNkGihWvqodMNPV3yiED+Ae6RzsmgNoandTlHDLSoN/N6KHYC0A39fcSeliCRqNpGd8hq3aDyo4ny2eLQS2HrC/KRE6txn7RY9WoqV8gwmNF9IXrebAVhywZtXjsBVuDcLPCpdx0wUG6EFyLEF3BXZaE7bJ0PFBKqwTZSCoCAJjMljCRLSFqGUhFLUQsU56v7Ljo431pmVAPmWjqT9idc8j8WIpOnX9uuyy7OTrJqtHVbxDSUyGsYim9JBLPVbQg02g0LdMoGLbsesiV3KrjncKrk0MGoGb0RbOS5arBmPx+3XACgN9H5ng0IPwSEasqGJbdL3hd0sUKMqGgWuGQqT1kUcuAzR2ykuOB0mD46ih3yCazZRyfKWDVYByEkECYbMnx0McjLTKL3EMGsA90QjrXszXXHLKuJPUb9Z/b4kPjewVRqiRaDXSdzv13SaPRnFW4Hm3Y1C9KluGyXaeo5ZClGgiyZk39A3Fb9oatG05i33hWOmSOS2GHhF+2VJFRF3Hem6Vel2LFRdnxqhyykuwh8wWZyCErOZ4UjvUcshMzBSkeo2rsRcWTglT2kNn+cHGgsz1khtHZ3YPt7AaMKIKnG0OzZVN/DTvvY6/fhsGEXXW8WzTagKBZXLQm1mg0LaGWAWtlbglnR83vqgelFD96YbxmPEWruNyNsgJN/cGSnYoaqFoLQogsW64fEQ6Zv4nBDO3mzBQdOdhblCzV6yKiJ0QMxw93n8L7v/K4dLSivG8MYIIsymdZiusXEGRJ0UNWxsmZoux3CwfDhgWpaG6XDtk8UvqbYRqko83qfthq8/t22yET4tGqcUFedf4odqxpFPO5uLTTm6fpLFqQaTSallAFWc2SJW+ab6WPbNfhafzKV3bhZ0em57yemj1kjUqWjgvbbBzKWSXIyp58LrUfqD9mM0Hm+Lss1TUBkM6YR4Fc2cXDB6Zw3wunA/1dQnSJYNiy4/nBr4p4ilgGBuI2Ts4WMZ4pynVGTCNUsuSClJdJYzKpn+eQzSOlvxlmh3uj5l6y7NSK6rOURA5pQ+hqOov+FWg0mpZQXaeaJUsu0uol5dc6V7icV4+DkzkcnsoFjrludQ5ZUhlrFKZU8WoOFldZNcBKgaKHLF/2R0UFnLi4BdejmC2w1xGXTf3VDhnAXu90ngW7ioDXiGVIwSSCYT3qlxvD44lGUhE8f2IWlPr9brZlBIaLV5Us+euNLoJD1undg+3sBgw29Xcxh2wJREm0MwFB01m0INNoNC2huk61HTJ2rJXGfjVvqxU+/O9P4w++/VzgWC2HzDaZ61SzZOm4UgDVY8eaQazoj8kkerWp3zLVHjLmRJ3JsVR/2dTvqg6ZKsgczOTZzxM8eDZiGrJ8GlHKl0LIxUPiaVkqij0nMwAQcMgqLoXnUZQqbpUgjVrBkmUnHbJO52u1MwQ7mNTfjdFJJPC1l2k0CF2zuOimfo1G0xKqyAnHXjiuB3GoFYdMNLYXWhRkR88UsKwvEjjmepQPag5+kKTqzLNsxSG79cq1eOcVazGe5mn9sqnfq9mrNpUtwzaJFGvqLsu0ImDThQpmajlkyi5IIZqk62aHBVlEupBSkHHBVfHY7syozYSdeG5xu/ja0R6yDo8Easch65WSZa0esl7DL1n2/lrPdrRDptFoWkJ1yMLBsGXFMWulh8x3yJon+7sexUS2JMtw8jilgcgLQV/MCoghQdFx5a7DehA++FmIISEY3ZBD1h9n/5edypWZmOIfZpUGDtk0d8gm+fDyiGXIaAvVIROCLLz5QITDAlCa+tnzlh0PZZcJzqjSVxaOvej0LstOmizCCW3lObrukC2hnYuG0VuTA85ltCDTaDpAtuTgueOz3V7GgqKKnHDelirQWknrL7VRspzMlgKRGwI3lA0m6OMN91XPWXGlI9UMUS4Ugqzi1XbIznBBJsRaoeLitn96DE8emQ70kKWVHrKJDBNkUctATJYUiezzCvelCcROy6GELW8TwiNfdkFpcOcmoA4XX4Qcsg7PaGwnwDQ4y7JTK6rPUhpHZJDGG100i4cWZBpNB7jj4UN4++cfrhkP0U2+9OBBfO7+/XN6rChZDsRtOa5HoAqyVkqW5TZKlqdmi/z5g+cNN9oL+qIWsjV6yERJrxWilgFClKT+0HOJ5vmpbImXLNltJ2YKuH/vBB7YNxnYsDCdK8v1S4fMNP0eMtOQOyGFIAuXF0XJVp0oIISWOipJuGGW4X/QLkZTfy+VLO0uO2RkCTX199rkgHMZLcg0mg5wfKaAEi8j9RI/3H0Kdz9zck6PzRQdWAZBKmqhEnbI2i1ZyjDTFgQZ7+cqOV5gM4HreTBrJJ6LjLAwxTYcMkIIErYpBVklHHvBU/ZzZZeXLNk/paIseSpdQLpYkcn5R6cL8rHCIRO7LG2TfSBGTLa2dJ0eMuGQqYJMOGRCLKsOmRqAK8ReouPBsB07/ZLKIRNCbCkIMkKWRmn1XEALMo2mA0zyD92wk9Rtyq5XNcqnVbJFB30xCxHLCOwmBMIOWXORJeZKtuKQneaCDAiKPcer45DVa+rnw7dbJR4xkVd7yGqMaALAS5ZsHdM5VpY8OVtEusAGnEdMA0fO5OX9J5QesrhtSlEl+tIOTrJ4j3AP2Sh3yFargowLj6wyKkkKMuXxQux1tIeMdFb8LMUcsqUgdAzSWSGtaR0tyDSaDiA+dEvu4s12bIVSxavpHrVCplhBX8yGZZCq2Iv2HTJ2XVpp6j+lCDJ17WyXZfUnSSpau4esWHGb7rJUiUdMFIVD5gaT+qOWqURK+LMJRZ/Yqdki0kUHfXEb/XELR6Z8QSYa/5lDZsqy4xUbhpGImHj04BkQUj3iSThkKwf8mZu+Q+aPShLH1Mdfu3kZfvM1m7B1ZV/Lr79djA6XLOVuwDab+nUOWWNM0tneP03raEGm0XQA0ScU3o3YbUqOi0zRmdPIokzRQSpq8XE99R2y9nZZttJDVpLfZ9twyNxQNEex0qZDZpuBXZbhgdCibGmbhnTPZmTJkjlk/TELfTFbOmSqNoiYBt5xxVp8+HVbATBH7LUXjIFS9txhIbFuOIHfvm4z3njJKnlM9EpllR6ycNQFAAwkbPz+jVsDvVULTedHJ4mvve+QkTb63boN6bCzqWkdLcg0mg4wmWFOSe8JMg+uR1vO/1LJ8JKlbdZwyNps6m8nh2xcccjUc3serdtDBlTv9iw5bTpktinnSjouhRlqXurnz6OWLM/wkuVMvoLT6SL64zb6YpZ8nSv7fXfLNgkuXzeEd1+1Th676eIV8rnDGAbB7/78BTVLlhm1ZFnDIVsMTKPTo5PayCHrclO/eFtaNd6fvYbeZdk7aEGm0SwwuZIjP4B7ralfCKFWRxappHnJ0jaNxrEXC5xDdipdxAouZLLFsENWO4cMqN6V2bZDFjGVpH6vgUNGpPMkwl8B4MRsEf0xW0ZkAMD6kSQAJqRqiZfXXjDG4jDqDEAPE3bIIoGm/s7tqKyFQUhH5yG2tcuyy039Mql/CThPOoesd9CCTKNZYES5EuhBh4wLjFqjhZqRKTroj1mwTFK1WaHkiiBSo63RSSWnBYdstojNYym2hlKwh6xeDhngi5RDkzkUyi5Kjtuy0AF4ybLswvMoPFrdDySexzINWToVuywF/XFLCkTLIFjJZ1BG65QOk1ELP799RaBPrBHSIauxyzLSBYessz1k7GvbPWRd+JRbarEXS0E4ngvo0UkazQLT04JMOGRzaOxnTf0W7KyBrBN8vHidw8lIeyXLcmNBlis5yJQcbB5L4cH9kyGHzKvZQ5aS8xwroJTiTX/7IN7/yo0oVry2yniJiIVCpSDHRIX7r0TJMqKMPRK7LP37+A7ZYCKCAe6qNRJLn75lR81ZobUQwiPbAyVLo8PxCX7sRSs5ZP59urHTcSntsiRakPUM2iHTaBYYkTMF9FbJklIqhdBcHLJixUNCNvXXLlkOJSLtJfU3ccjEDstN3CHLKpEd9Rwy4YIVyh6P+XDw1NEZAMEoiGbEuEMmyrPh51JLlqJXKFNyMJiwA/cRDtlQwm5JkMVsU7pvzRDJ++I6iVmWQDcEWWdHJ4lyaCvPwXLdDLmuxaadzLRuY5Cl4eSdCyyBt4tGs7SYyPouSTcdsrufOYkv/PiA/FndGdlu9IXnUT4rkZXn6uWQDScjbQXDNushG+cp/ectS4KQYMaZU1eQsX/WSo4rz//8iTSA9kRKgveQCYcs7MbJUqSyyxIAlvfFpHsmdlkCTKwKt2yhdjtuHEliIG7jsYNnAAST+s+2kmU7PWSA//q7Yf68cssy3HbNBqwciDe/c5fptJDWtI4WZD3IdK6M2fzcwjs13Wcy0xsly2/+7Bi++ugR+bPar9WuICvLHjGWm1Uvh2yoxZJlma+lWclyPMME2YqBGFIRK1CybOaQFSue7Jk7zX8nbfWQRbhD5tYWZEJcsZKlf1sqZskP4n6eQwYAgy06ZO1gGARXbhyWu0EDwbCL3NS/WIKs1TKg+J10wyFbM5TAJ960fUk4T6beZdkzaEHWg/zuvz2ND3/z6W4vQzNHAj1kXSxZTmVLgViJkiIO2y1ZimT9qGXANggqLgWlFH/8H7vx3PFZ3yFL2ChWvKYzPEstNvVP5yr8vBEko1ZVybJWD5kYj1SsuFUOXDuCLMZzyIT4tMI9ZIGSpX9bKmphBW/K74/ZAYdMCrIFzAN7+Xkj8vuoZUgX8KwrWbbR1A/4oldrjcboHLLeQQuyHmQiU8Jkttz8jpqeZDJbkh8G3XTIJrPlQPCqKsjSbQoy0esVtVlJzHE95Mou/umhQ7hvz2m/hyzJxvvkmjhf5RpN/ePpIn7v354OrDnP+9GSUQup0EikeiVLMUC86LhVPWrtliwBv2G+2iFTcshCg8dFTMdA3Jb3G0zaUsQtZDnxakWQBWIv2oj4WAgWyyFrNevMF2RabDRCj07qHbQg60HKjtdSHICmN5nMlmV4Z7cEGaUUU7lSUJBV5l6y9B0yE5ZpoOxSee582ZFO4LAQZE3KljKHTLk+P31pCv/2xDHsOZmWx7IlFxGTiYxU1KoanVQrh8x3yLyqSQDtxl4A/rWqcsiU2Au1J6wvpjhkcau2Q7aAgmzrij55XtZDxj5dxfzKxeJtl68OhNwuNO32kInfidZjjWE5ZPoi9QI69qIHKTkuPKq18lJlIlPC2uE4Dk7mWo4vWGjyZb9c57geLNMIlSzbFGTCIbMMREwCx/Oku5UrO4piVBEAACAASURBVHL34mCiNUEm1uJ6FBXXg20aUjyp7nC+7MidhOGh4U0dsholy3YcsnhECDLmJlY5ZHERe0F4Sj1AKStZ3nTxCkzlSliWjGIizkrY6i7LhSwnGgbBVRuHcd8Lp2GZhhRii+2QvXHHquZ3mgft5JABrCxMSHdmWS4ldFJ/76AFWQ9Scjx47Y8a1PQIk9kSrto4DCBYJlxMphRRU3Q8pKoEWZs9ZI7fQ2aZBiqKi5svuUhFWZJ9n8wAa80hA9j4JNs0pMBTe/CyJQfJCDtnKmrh1Kw/Rsmtk0MWtdgHcaniyjVGLANlx5unQ1a7qV84MbZhoOx66IvZ2LqiH598y8UAgE2jKbz1stW4dsuoIuIWViz98jUbsH4kwc7dpR6yTiPT71sUDxHL0M5PCxDS2ZFXmtbp2N9YQsiXCSGnCSHPKceGCSH/RQh5kX8d4scJIeSzhJD9hJBnCCGXd2pdS4GSLlkuWfJlB/myi1WiZNklh2wy54sa4TyJkiUh7QfDSofMNllSv0el+5QrOyg7HiKmgSQXZM3S+suuJ50Osb4CP9+UIsjyJRdJ7pCloiGHzKU1P5wJIYhaBoqOJ9e4dUUfALQ9XByo75CpSf3sK5HrVInZJv76nZdi9WAccduEbZIFj6R4xaZl+PgbLgSAriX1d5p2hosDTPRq46c5LIes26vQAJ3tIfsKgBtDxz4C4D5K6RYA9/GfAeAmAFv4n9sBfL6D6+p5ShW35xLeexnVUek2wpkSo2+69XtUHTLhPJWU8Na59pDFLAMRHgxbkD1kLsqui4hlSPHUzCErVVwpaMS5a5Usc2VHirxUrDr2opZDBjARxEqW7JwXruwH0F4URCIScsjCw8WVkiW7nX0V+WS1IISgP2Z3VCx1K/ai0/g9ZK3dv968UE2QN1+yCrdcvqbby9Cgg4KMUvoAgDOhwzcDuIN/fweAtyjH76SMnwIYJISs7NTaep2y63Wt1LXUeOFUGjs/eW+gEbybCJGSjFqwDIKywxrLP/Gd5wKDpzuN6jIJUSLE4bJUZO4lS9uEZRig1O8Ty5fZfyBE4z3QQlO/68l+KnHNfEHmrz2nlCz7ohayZQeUUnmOesImZpkoKU39b7pkFd5w8UpZ1msF0UOWLrBrZYZKlnHbxHtfvh6vvmAUgF+6bCTIAOCXrt6Amy5a0fI62kUIxLOtZEnadMhs7ZC1xNsuX4NfvmZjt5ehweLvslxOKT3Jvz8FYDn/fjWAo8r9jvFj5xysyZlqQdYiJ3lPkdpb1E2EAIjbpuxb2n0ijTseOYyH9k8t2jqmlJmKomwn3lMjyei8mvpti48J4ufIlRwpyITr1Ujwefw9LgRZsYEgy5dd6VSlYhYohQxBVW8LE7MNHnvBXvOWsRQ+957L2+ohG+IbFCb4euyQQ0YIwZ+85SK8bD3rF/RLlo3HHv2PG7bgxos69//Ns7aHTMZetHZ/3UOmWWp07W8sZf/Nbbt1nRByOyFkFyFk18TERAdW1l3Kyu6zZuGaGr8vqlDpjZ47IX5iXJBVXN+lObOIDpkqasS1EaJqWV8UmWJFOk2toDb1C2GSVRyyiksRMQ3pDjUSfKKvTpT8xDUT61TLrdmSI103IXTE8xbKLuJ2bTcqarGSpXh/tDPDUjCcYoJsPM2uZbOdaKKk2cwh6zRil+XZ10PW/ugkLcg0S4nF/hs7LkqR/Otpfvw4gLXK/dbwY1VQSr9IKd1JKd05Ojra0cV2A7WZX7tkzQmXuzrNVLaET92zp65YFuuJ2azXquz68RBnFijs1/VooEerVshrYJdlJdhDNpqKwqPNw1tVZA4Zb0oHfBcsX3ZQcjxELBO2aSARMRsGz4p1VJcs2fEqh4z3pYn+tEyRlS1zZaexQ1bxS//tNPML+qIWbJPgNB/fZJuNP9zF7aluC7KztIdsIG7jLZeuwlXnDbd0fxF7odEsFRZbkH0XwPv49+8D8B3l+C/x3ZYvBzCrlDbPKVQRpgVZc8LuSqd54MUJfOGBl7BvPFtnPUKQMXFScvzm9+kFcsi+8vAhvObT94NSioOTOVz6xz/Ec8dnA/eZypWkCJEOWUU4ZMz5aaePTC1Zil2FfsnSDfRz9cdspAsNHLKQICuGXM7pfEUK3mzJb+oXzlO2xAQgpX6fV5io0tRPyNxiJgghGEpEcLpVh6zFHrJO062k/k5jGgSfeddl2L5qoKX7s12WWpFplg6djL34FwCPALiAEHKMEPJ+AH8G4OcIIS8CuIH/DAD3AHgJwH4A/wjgA51aV69TDgiy3ijD9TLFkLvSabIlPwy10XpiNpspWFYE2Zncwgiyp4/O8LFIHo5PF+BR4MiZfOA+U8q0gLBDtiwVBdBeOGxRmWUZCQmyAhc+opm8P25htlBf7Pkly9o9ZAC7VhXXQ9nxlBwyXrIsOrKPLFnXITN57IWLmGXOebfdcDIiHTu7iaiTuyyb9JB1mkF+XcXXc5XBhF0VQaLR9DIde7dSSm+tc9P1Ne5LAXywU2tZSgRKloskMpYyi12yzPNSYb1YB3XEkGjqlyXLBRJkBydzAJgoFMJQrOeOhw9hx5oBTOXK2LayHwcmcjVLlsBcHTJTNq+rZcnZfEU6b/0xu3HJkq9HBKvWEmST2bLs+5KxFzJ0tiJnXCYitf8Ji1kGTvOk/rmUKwUjqQi8U+z7Zg6ZbRowDTKv51sIdqwZwLc+8Apcunawq+voNh94zWa884q1ze+o0fQI+r8PPYbq9OiSZXNkybKNfqj5IOIcsnXcJTHMOh7hgsxdWIdMlCkBFpoqhIlY15/eswdbV/ThTK6MNUPMIfNzyFyYBsFAggmhdsJhS44HQlifVLhkCbBy7KpBMb/Rln1XtRAOmV+y9MvOfTE2r3IyW8IgX6dwwdQNA+I11StZqjlk7eysDDOcjMrvm/WQWSZBX8zqevYVIQSXrxvq6hp6gYGELd/rGs1S4OxqMjgLKOmSZVvUclc6iWiEr5ezpfaQyQDVBXTIJrIl6Yblyo5MxBfREyXHw9PHZuF61C9Z8vdU2fEQtQz0t7ATMkyJP5YQIkuTqsM2k68oPWRWWz1kalP/miGWEzaVK0mxGXbIciW/ZNmsqb/I1z1XRviwdAAwawwyV7ENQ5fINBrNnNGCrMc4V3ZZZksOPnPvvnlHe4QbwjtNrknJsqgm2vOSpRp70U7URC0OTuTk9/my75BlSk7VmoQgU5P6o0pWWLpBn5dADEcvKU6TiHdQBR1r6me398eblCz5+1o4XvJ3WHalqzeZKct+vWQ0WLrMKoKsblO/ZaLkzN8hE1lkQPXopDDMIdOOjEajmRtakPUYgab+s7iH7IF9E/jMvS/iuRPzS9hf7KZ+4ZDVE2SFigvLYGU92ww29ZcdTwoJAaUUXhuT5EW5EmBxE6pDJsqoQuiM9kX5TEexy9JD1DJbTtPffzqLC//wB9g3npFiDgBsK5hDJhDN/myXZXXO2b7xDP718SPyPS7Cc4vK6KTRvigipoHJXEn264k+sYjFNkpkSk7zHjIl9mIuGWQCkUUGVA8XD3Pd1jHcuL1zCfwajebsRguyHuNsK1nWExzCnck3EQXNKITmIHYasd5GJUvhyEQsHnuhiLBw2fLPf7AX7/rHn7b8/KogywV6yFxkSuya/vqrzsPl6waxbWU/66VSesiiNssJI6S5INt7KoOKS3FoMscFGXtdthFM6hfIkmXcqplz9rVHj+Bjdz0nf1cRy0DMMgJl54RtYlkqwh0ydn61DNjH51k2L1maLKm/4iK2QCXL8CzLML/6yvPwP27YMufn0mg05zZakPUYZ1sO2Tu+8Ag+/cO9VcdFSaudcNJaLHbJMitLlrWfT93VF27qB6oF2f7TWbw4nmn5+Q9O5uT582VfmGRLjhRIl60bwrc+cA2GkxHEbVNJ6vf7wJIRq+5rEIynWWN+ruwwMRdyyDLFSqA/K6rkkAHVJdHpfBmuR2WMRMQyZPM9pRQFLmZHUlHeQ1YtupJRC9mS0tRfx/2K2SYoZaJxfk39rZcsNRqNZj5oQdZjlCpqD9nSd8j2n87imWMzVcdF03e+Tp5Xqyx2U3++SVN/qeJKJylqBnvIgOrxSZliBemi03Jv2cHJHLat7GdrKLu+ICtWlywB1mOlzrIUa0tGzaYO2TjfKZktOqzcyYWgECYVl8pMM0B1yMQuzrAgYz+LUURRy+TrY6OXPB70uiwVwUSmVNMhS0WFQxZs+A8jxOFsvjy/2Itk6yVLjUajmQ9akPUYpbOoh4xSinTRwcmZ6giEtBy7s7QcsqZN/Y4rG83FLMtCxcUy3osUHp+ULTlVo5Dq4XoUh6fyuIgnledLjlxPruzUFDBRywjMshSiKRm1kG0ihkVCfbbkBkuWSkCq6iCpPWQAqnZaznAxeoo7b1HLQMxiDp5YY9QysGowjhMzBb9PLCTIMiUH+UrzkiUAzBQq82vqb6NkqdFoNPNB/wvTY5SXcMnyJy9O4InD0/LnQsWF61EcnylUOUCyh2zegswLfO004SDWWusJlCx5D9kqvuMxPD5JlBln8s13PJ6cLaDserUdspIjYyhSVQ6Z2tTP1paKWs0dMi6csqVKsGSpCDJVsKg9ZEDtkiUAjM8W5f1j3METa4xHTKwZSmA6X5GCMKEIKtFDViizkUj1Ii2ECMuX3XnFXgwlInIeonbINBpNJ9GCrMdYyk39/+fuPfjMvfvkz0JslByvqndKhJLOt6l/0XPISo1LlmJUDwBll6WHsb4oLINUXQch7BqNGhJM59h9RvuiSERM5pApwbAZfq5+JXohZvmCrOz6giwZaV2Q5aRDxkuWijAZiNsywT4S7iELlSxn+PpFKTSqNPXL/DbLlNEXe8czSERMGErvVipqIcd75xJ2/ZFIqgibj0NmGkSOINI9ZBqNppNoQdZjLOXRSTP5SsDpUYNDT4TKltIhm6eQEqWuTiT1lx0PX37woMziopQGBFC99ai7LMsuc3/iEQtDyUhAkFFK5TVqRZAJgdMfs5CIWMhXXORLwR4yyyABMRKPKE39FbWHrHlTv3CoMkUHRaU3Th3UHee7NtXjIvBVdcgqricF46nZ6qb+QsAhY4Js33imKtYipeyyjNeJvACCImw+ggxgZVnTIF1P4NdoNGc3WpD1GCXHg2UQGMQfMbNUmCmUA8JCHc1zYrYQuO/sAsVeyJJlB9zEhw9M4n9/73k8fGBKPpeovLZUsjQNVFyKXMlBwjYxEhJkJcdDxWUnbEWQCfHWH7eRjIYcsrKLdLGCVGh0T9xWm/pd2ZifatLUrzpuuZKDkuO/LtUhi9umHP4tHDKxqUD9/atCfSpXktdHrE+sMW6bMq1/PF1CKhoUU6moLXPI6vWPAQg08s8n9gIARpLRpnMsNRqNZr5oQdZjlCoeYrbJk8aXjiAr8kHOqrBQc6pOzAQF2YLHXtQ4z6e+vwcf+OoTbZ9T9LuJ1yLWLkRY3DYbDBd3ZRCpECjpYgXxiImhRCTQQ6Zen5YcsoK/izIRsQI9ZAATMOoOSwCI2kZVUj/AHLJGgux0piS/z5acgLum9pDFbBOJaPD1WqaBZMQMOGQzedUZZKVAyzQQs9mmA7HGqG1gWSoi1xl2yPpiFsoOe581FmT+bfMJhgWAoaQts9c0Go2mU2hB1mOUXbYTLmobgQiMXkd8+KaLFRkEq34gVwkyLi7mW2qUDeuOVxVA+7PD03j66GzL5yo7Hn7nX5/CTX/zE7ZGLpjE2sWuv+X9URQrXs2xT8WKK7OxhKgocpE9nIxgKqcKsuAsyGakVYcsYvKkfgd9fBfieLqIVDQ4uidum7IMru6UTPE8LwGlFP/y2BH5GkX/WMQ0mCBT3DVb2W0YVRwytVQaHp80HXp9orwpcsXkUHbeFybKluHZkOLn0+lS3bFJAGQfHzD/kuVYX2ze59BoNJpmaEHWY4idcFGe8r5UmOHiS4RxAv7XZMQM9JCp44Ry88ghq7geHI9KpyR8vSYypZbmNQIsUuL2f96Fbz15HC+cysBxPflYsXYhYMb6Ynzt1WKy6AR3WQriXJCdyc3DISs6IARIRSwkomyAd8nxMNbPssBOzharHLKYbfoOWSUYe1FyfFG5+0QaH/3Ws/jh7nEAviBbP5KQJUs/GDZYsoxHqnvL2Pgk//UJZzARCbppI6kopvNlmaEmhI8oWyaqSpZckGVKUgjWIlCynEcOGQD8xms24e/effm8zqHRaDTN0IKsxxAffEutZKk6PEJcCAdoy/I+HFccMtUZmk/shXDHxADocBbZ6UwJGZ7z1YzdJ2bx33snsGUsBYCJH+Hw+A4ZO78QQLXKluFdloJ4xMBAnM14FE6e+vjZQnD3ZS3ShQr6ohYMgyAZMWXivRCIk9mSdMvk89omio4HSmlVyRLwd42K349w8ERD/6bRFHfIfHdNzeOK2aw8CQQFaH/cCjhks/z9sXFZEoDvpo2mIqCURXqI9QLA2uF4YJ0C8fNUrolDpjb1W/Nzt1YPxnH1ppF5nUOj0WiaoQVZj8HynkzukPkC44Nf+xn+5t4Xu7iyxqgOzwwXF5miA4MAm8dS8gMXCDZ7zyepXzSCDyZs/rN/vXIlf6xQKy6ZKKlddd4wew35snR4xIYEIaCW98fkc6hQSoOzLM2gQzYQt+FRyEBWVZi2usuyj0dKJCKWL8i4QKQ0mEEGsF2LrkdZidWjSsmSfRVrOckFmej1Gk8XEbdNrBiIYbZQgetRJYcs6JCJ4NaAIIuFS5bsvOeNpgL3HeFJ/8emC3K9gO+QJUOiSziAlNYPhQWC5dPoPB0yjUajWQz0v1Q9RslhI2pYD5kft/DjvRN44sh0k0d3D7VpW3XI+mI2Vg/GcTpTkqG3QiDFbVPGNsyFRg6Z2pTeitgR99kwwhycmUJFCoqTM0V4HpVrXV7HISu7HjyKmiXLGBdkgO8WiZLlWF+05aZ+MZYoGTXlDs2xPn98UVVTv7KxAPDFie+QcUHGw1pleGumhOX9UfTFLClsxWMJITKTK2abvkNmhnrIAiXLCmyTYDUPyBXXZllIkAk3S/SQhR0ytaesoSBbQIdMo9FoFgMtyHqMsuMhYhqImH4P2Uy+gmzJCYieXkMVFL4gc9AXs7B6MA5K/b4kIQ5WDsQWpGRZyyGbaFeQ8Wu7bjghHyOEY9n1MJUry3432UMWEmTCsYvZ1SW8eMSUYkq9PgCwdjjRUlN/plhBPxdcarlOrAdAdVM/v594znDJMhsWZCK8NV3EWH8sIIiiirAR0Rcx25A7IYMOWbBkOZMvYzARwXCSrU+ItxE+UurYdJ6dL8KO+w5ZdQ6ZfG12qz1kWpBpNJreRwuyHkM6ZJa/O+7IGfZhFR6700uogkJ8L0psKweZYBB9SsI5WTEQm1dTvxBAwiErBhwyfxNBOw7ZuhEuyPJs6LdIOzgxU5ACTPaQFYNrF7tiawoyxSFLhwTZqsF4y039omSpChWxHqDaIRPukPidqLssAdUhY78b8R47nS5ieZUg81+P6I+L2SaS0eqeuX7eLyciRKbzZQwlbAzy35VwsFSHjBBfqK0VuyxDr6evRYcsYhpy5NF8m/o1Go1mMdD/UvUYsofM9h2yo9w9EKNnehE1F2pWRmAwh2yUl9REz5NwTlb0x1Aou1VzLltFRCUMcYesUPY3QbTtkBUqiNumdJtm8mVkChXZhM6GXfOmfn6fcMky7JBFa/SQqevJlthzjiQjLZYsK3JOpCpGRhuULOs6ZJGgIBM7Sc/kyqCUYjxdwor+aEAAqU6TKsgSNWIvhhIReBRyV+l0voLBRESK56gcRG4hYhoseV8ZhTSSiuKzt16Gt1++JvB6wnM660EIkWJUO2QajWYpoAVZjxGIveAf8EfPMPciU3LkGJ9eY6ZQwfL+GKKWESjJ9ccsjCSZYBAfzsIhWjEQg+PROU8kEHEOgzUdsvYE2Uy+goG4LUuCoods6wo2yPv4TAHZkgPbJBjmA7WrSpaOcMhEPIRSNouYGEhUlyz7YhYG4jYyxea7QVnJUvSQ+cJkJBmVTl44t0vsWhTlbuHapWTJkg2AF+XkmXwFmZKDQsVt4pARef5auyyFkD00lZPPP5Sw/ZKl5fejibJlPCSc3nzJqoDYFPcRr7WRQwb4vwftkGk0mqWA/peqxwjGXgRLlkBrAaLdYCZfxkDcxkDcVprWWclSOFiTWS7IihWYBpHlqrk29sumfv4hXwj1kAnh1KpDNpiwYZkG+qIWZvIVpAsO1g4nELdNnJwtIl9ykIhYskQXziFTB2QDtXdZquvJlBykYpbsgWu0G9TzKDIlv6lfFSPJqCmFU1iQiUb86h4y/hpKDqayJTgeRTJiYjpfxjjvJxvrjwXOp+5WFNEXMdvEBSv6sXIgJndMAr4ge2mCCbLpfAWD8YhfslTEmxBkrThZhBC5pkY5ZOr5orqpX6PRLAG0IOsxyo7HkvqVYFjR8AygZxv7haAZiNtVDpllGhhM2DjDZximC+y4EAVzHTBeqDR2yNYMxRG1jJZiL2YKFSl2BhI2TmeKKLseBuI2Vg3GWA9Z2UUqaiFqmbBNEgh2Zc/foKmfO0mmQUIOmS2F2kyDdWbLDiiFdPBUMZKMWFKkiB4z9XkBRZDZ/nBxgJVdT3ABtm1lP0qOh4OTTEQt74sGBZkibMRri9kGrt40gkc+en3gvmuG4rAMgoOTOVBKWVN/0pYlS/XaCGHeqpMlXmOjkiU7Hxdk2iHTaDRLAP0vVY9Rs4fsTF66KOERNL3CbIGV/AYTNmYKrA8po+RmqYO100UmfkTvUasDxvecTOOmv/kJHj4wCQCypFurqX8iU8JYXzQgEBuR5usH2K5N4Ur2xy2sGozLpn7hTNWaBVmoBEuW4V2WhJCQYGW7JgdDpcx66wMgS5Zqgn0iaiqCrDqpH1Cb+g351TIIciVHZpBtX8XKs3tPZQCwvDW1Z0t1tUTsRbjMKG83DawbTuDQVA65souKSzGUiGAgbrPmfdUhSwpB1pqTJYR8s5Jl1PJdPI1Go+l1tCDrMQIlywrr7zk+U8DFqwcA1N9pefRMHk8dnVnMpQaYyVcwKEqWBQe5sguP+gJhJBn1S5YF1gslPlBFs/wTh8/g9X/zE5xOF6vOfyZXxq/duQt7Tqbxe//2DHLK/MMhGXuhNvUXMdqGIJstsPUDwEDcln17/TEbG0aS2H86i5l8RTpLyUi1ICuGd1mGhnCLc8um/qKDVNSqKmXWQrhxoqlfOGSWQRAxjbolyyqHTOndEqJSdcgA4AUuyMb6o1L8sMeqsRdc2DUQOxuXJfHSRA7TXIgPJWyYBsFg3A5kgy2r00NWD/EamzlkYm06h0yj0SwFtCDrMfzYC+aQnUoXUXEpLlkzCKB+yfL3v/kMfu3OXYu5VInrUaSLFQwkIhiIR5AuVGQKvXDIhgMOmYP+uCUdMhF98cPnx/H8yTQ+c1/1RIKP3/UsTmdK+MSbLsSJ2QL+4gcv+E398WAwrMNzw0b7Yi0LMtHUL84nHtMft/Gq80eRK7t44vC0FCh9MavGLsugIIuGSpbifLWa+tka6pejww6Z6hKpfVXNd1n64oQNGHdxaraAqGXIvq8XTqXRF2O/nz4l10wt/UXMxg4ZwATZoakc9p/OAgBWDLAoi0+9bQduu3aDvJ9fsmxRkCnTChoRswwYJDhZQKPRaHoVLcha4IsPHMDH7noWAPvQ/fG+iY48j+N6cD2KiGkiaplwPIrDvJ/n4jXCIasWFydmCnjkpSlMZEoyWmIxyRQroBSyqX8mX5aOjnTIUpHALkvVIRPC6tljswCAf338KA5MZAPP8djBM3jLpatw2zUb8Y6XrcVXHz0iBVEyynq6hCCbypVBKVouWYph56J0KHZDAqxn65rNI4hYBsquJ52pZLRakJVkD5kYMcS+WgaRJToxz1Jct1TUxgAXlI163dLyegbFiHTsuEAL53YJd0jsolRFVTJqSods1WBcboI4OJmT46FitiF3NQZKlmZzsbNxNIlixcM/PXwIyYiJqzaysVQ3XrRC7l4F2mvqB/wssua7LE3ElCgNjUaj6WW0IGuBu548gW88fhT5soM7HzmE9335Mew/nVnw5xHxD2J0EgApTC5Y3oeIadQsWX7nqRMQUV77xhd+XYInj0xjqobgE4JHlCxzZVeWqfySZQTT+bJ00/pjdmC3oudRPHt8FjduX4GYZQTmdmZLDqZyZWzgDs6VG4fheBT7xjOwTQLLNBCzTOlQiQyyVkuW4nbfIVMEGe91u/o8NlzaF0BWdVO/UzsYVnWRxHpcjyJXdgMOmSq2ixUXt37xp/iz77+AfNnxHbJQyVLtaYuYRtWOQpF8/8KpDKKWgWHebycekys7OD5dwMqBGIa4IPOoPx5Kdd/Uc9smaSp2hOP2wL4JXLdteV3BJRyyZiVIgSxZNhFwMdvQ/WMajWbJoAVZE0qOixfHM3A8iicOT+OBfayh/GeH59avRSnFr96xC9975kT1c3GHReSQAcDe8QxMg2DVYJw1zIfCYSmluOvJY/LDb9+pzgiyiUwJb//8w7j5cw/JXXgC0TA+mLClyyRmE4qdiyOpKCgFprIlTOcqGEwGm/oPn8kjU3Tw2q2juOHC5Xj80Bl5/iNTrMF+/TB7jRtH2dc9JzPyAzcW8QWZSOkf7YsGSoT1mOXD0PuVHjKBKBFev20MgO9EXbA8hb2nMtLdA2r0kImm8ogqyCzMFirSXeuLWYhYBtYMxWUzPQA8dXQGj7w0hX/48QG88bMPYoIL4f7QDkMhEDePpbBpLFX12qKWiT+5eTs+9baL8ZPff60UXQATNhOZEp4/kcb2Vf0BIbo8MI6pOvjVNo2mgui8Zf56Xn/Rirr3kw6Z1do/R8IFDM+5DBO3zZb70jQajabbaEHWhBfHs3B4YOd/753AY1woPDnHBvrTmRLukg//VQAAFt1JREFU3TOOLz14sOo2sasyapnSjdh9Io31IwlELANDiQhmCkGH7IVTGewbz+JXrt2IwYSNvePZqvMCzGV6eP9k3XUdm87jrX//EA6FxJbg/r2n4VEmvn7hHx4O9DvNKA6TEDNiuoCIaRDlsCePzqDsetg0mgo09T9zjF3Pi1cP4oIVfTg5W5RC6sgZtqb1fKzReVx8HjmTl+InbpuyqX8fvwbnLUu2FLoqHT7uHg0qJUvh8L32Ai7IuIi8dssoyq6HRw9OyfvK2AvLL1USUu2QpYuO0mPHznfp2sHApownDrNB8n/85u14aTKHHzx3CoAvRiIWm3cqruFvvnoT7v7ta2u+vvdevQG3XrkuMPNSvJYXTmVQdj28cssoy2Dj5x/rVwQZPxZO6m/mPi3vjyJum4jZBl59wWjd+43O0SFrVrK87ZqN+PgbtrV0To1Go+k2WpA1YfcJ1tc01hfF1x49grLjoS9q4ckj03M6356TaQDAk0dmcHK2gM/e9yL+6Lu7AUAGwUYtQ7ore06msXmUOQ2DCbuqh2wXF4jXbR3D+cv78GKNkqXjeviNf34C7/5/j8p5kmH+4ccH8OSRGfzohdM1b7//hdNY0R/DV267ApPZMn64e1zeJsTZYMKW/VfCIVNjLwDgpy8xAbNlLOU7ZGUHzx6bRdQysGV5Chcs7wMAWRY+zB0yMWeSjeBh51XT2IVbtftEGqsH4xjkMQsAc82+/eRx/PsTx6r604TDNyAdMj+8VIiOtcMJ/MnN2/ELO9konys3DCNiGXjwRV/kFiouLIPIHYiEsB2QYUGmJuOL63PZuiEcnynI448fOoMtYym868q1SERMPHV0BomIGZgXmYiaUiASQmAY7fVKCYcpYhm4kvd3CeG8XJmPqd5PwEqWjf/5IITgsnWDeMPFqxo24A8lI1XCtRFvv3wN/vfN25sKwkvWDuL1F69s6ZwajUbTbbQga8LuE2mkohbeevlqFCouIqaBW69ah33jmarYgwMTWfmBWo8XlLLU5//7AP7mvhdx5yOHMJ4uoswdsohSsixWPGxZzgTZUCJStRPv6WOzWJaKYNVADBcs78Pe8UzVbMg/vecFPMjdsWdqOHsTmRK+sesYAODZ47NVt5cdDw/sm8Brt47hZeuHsGYoju8/d1LeLgRNv+KQCcfLb+pnH/CPHWQCcvNYChGehcUcsllsX9UP2zRwPhdke08x4XSE57D1K6GnokQbVxwy0dS/+8QsLuSZWmI9n/mvF/Ghf30K/+vfnsbtd+4KXCO1B059TL9SwgOY07R5jK0tHjFxxYYh/OTFSWRLDp4+OoNixa0SFRHLCJUsw4LVd8gAJtQ9Xh7fuWEIUcvEtZuXsfWEQl+TEatlV6kWKV5+vXLDsBQ3wiVc3h8sWdomgakIvvdfex5+73UXNH2OO3/lSvz52y9ueB/bNPDJt1yEW162puH9BOtGEvilqze0dF+NRqNZKmhB1oTdJ9LYtrIPr9jEPhR3bhjC1eeNwKPAM8d88XLPsydx42cewO9846mG53vhZFqKpzsfOQzbJPAo8N2nTiglSyPQr7OFi4ChZLVD9uyxWVy8egCEEJy/PIVM0cEpRRQemMjiyw8dxK1XroVtEjyjCK7ZfAXfffoE/vSePai4Hrau6JNCCgBOzRZx7/Pj+MHuU8iVXVy/dQyEENx00Qo8uH9SCpnHD53BslQUy5JRrBmMwzQI9o1nMZKMSIEinJfnT6axoj+m7BY0kS5W8NyJWezg0R6rB+NIRky5QeHImTzWDycCr3sj70/y09hZD1mu5ODgZE6GnAoB9N2nT+CSNQP46E1bcWAih+eOp/3rEG7q5+5bf6xxj9Irt4xi73gGb/v7h3Dz5x7C7uPpqlwu5pD5v0vxHHtOstcmQm2ZGCV46ugM9p3OIFN0sHM9c61E/5po6Bd8+MYLcNs1GxqusRHC+bp2yzJ5TDiPqkMmphOoXLlxGDde1Nx9skxDOoaNeM9V67GFC3GNRqM5F2n8iXOO43oUe06m8Y6da7Fz/RAG4jZet32FdDPufOQQ/uwHL2A2X8bhM3lELQOPvnRGptbXYs/JDLat7MdFqwewdzyD33z1ZvzohXF868njuHw9O2/4Q33zmChZMoeMUgpCCPJlBy+ezuB1vGHad5YyWMkzn+762XEYBPifN5yPZ4/PymiJY9N5vPdLj8kG/TfsWIkLlvfhr+/dh0yxgt//5jO459lTcg1Ry8A13Km58aKV+MefHMSPXhjHG3eswgP7JnDjRStgGARj/TE89rHrkS05vBTFXBXxQU8ppOMHMFHw0P4p5MsurtjABIhhEGxZ3ieb3A9P5XEJv+aCjcuYQBOxDnHbxEy+jBdOpUEpsH0ViwkRJdRCxcUbdqzEO3euw1/+cB++9eQxGSWiOnzsOtd2yMII5+rImTwIAR47dAZrhuKB+0SsYMlSnPOeZ0+iP2bJMNaYbeLClf148si0PMfODUMA/P618Fikmy9d3XB9zRC9Ya9UBJnYhTkWauqPtthwr9FoNJq5oQVZAw5N5ZAvu7hwVT+SUQsPf+Q6xG0ThkGwcVkS33/uFDYuS2LHmkG8bvsKvHLLKH7xS4/iJy9O4I07VlWdr+S4ODCRxQ0XjuE9L1+HQsXFr71qIwbiFv7oP57H00eZWFI//AgBNo2KkqWNikvlTMXnT6ThUWAHT/EXguzv//sA8mUXN25fgW8/dRzXbF6Gsf4YLl49iLufOYHpXBm3fP4R5MsOvnLbFVg/ksTqwTgeOjAJSoE7Hj6Ee549hXdftQ5vvHglHjt0Bsv7Y7I8dtnaQazoj+HbT57AyoE40kUH121dLtc8kooGBk0DzCkZ4j1wm5XdgPGIiZcmcrAMglee7wuD85encN+e06i4Ho7PFPCmS4JujHTIIr4gO1XxsPsEc77CDhkA3HTRSgwkbFy3dQz/8fQJfPz122CZBmYLFfTFLFmSkyXLWGNBduHKfnz4xgtwzaZl+JPvPY9dh6er+pqilhEoK4pzHzmTx43bVwTKgJetG8LXHz/CQ22jWMddwbH+GF5+3jBWDQbF3nx5w8Ur4XkU25RMMFGyHO3zf3/vvmqdFMsajUaj6QxakDUg/OGubrP//Ru3Mpfp6vWynON6FEMJGz/acxqv3DKK0+lioAxz4HQOjkexdUU/xvpi+Njr2Q6wN12yCp+8ew++9tgRAEFBtmYoLj/QxYfldK6MVNSSJVPh9AwlI/j1V5+Hbz5xDB/46s9wzeYRHJsu4Hd//nwAwI41A/iXx47gT773PE6li/j2B6+Rbh8AOZ7pb3+0H30xC3/whm1IRCy8YrMvlADmYL336vX49H/uxZlcGbZJAmWvegwnI5jOV2QJFvB3LV65cTgggM5f3odv7DqGZ4/PwvWojLwQiB4ysaMxZhvIVxzsPp7GUMLGygHm8AgBdNHqfqzlAuetl6/GD3afwu9842m868q1VY5m3DYRMY2mDplhEHzgNZsBsLBTJsiCTtKHbjg/0I+lPo8qQAHgFZtG8JWHD6Hievj467cFMr6+ctuVAfG2EKwfSeK3rtsSOPYLO9dg1WAsICwvWzeEy9YNLehzazQajSaIFmQNuHH7Ctz9/10rnafAbTVylUyD4DUXjOFHe0/jZ383jeMzBdz7O6+G61H84Xd2S9dBlKkEI6koXrd9Be5+ljXKRywDBOzDVxUvot9oJl/B2mHWgL+8Pxr4wP/oTdvw4ddtxV/911587v4DiNsmfv5CttYdXLh968njuH7rWECMASygc/VgHMdnCrj1ynUNd8bd/qrz8L1nTuLZ47N45ZZlVTMUazGSjOLARK7KIQPYLlGVC1aw1333M+yaiB2Wgg2iZMmFw+qhOL791Al8J3McO9cPSzEzmLDRF7Pw1sv8hvHrto7h1ivX4ntPn8R3nz6B0b5ooGeKEOaAbgg9ZyNuunglPnn3nqq5iW+5LFhWDAiyzcEoiJ+7cDl2/cENGFFKvYLFCjjdtrK/6v2p0Wg0ms6jBVkDIpYhe5Fa5bqtY7jryeOglAm0v/jPvTg1W5S5UlHLqPlB/56r1klBFrVMiM9jVbyIPqwPfu1nSERMHJ7Ky74uFdMg+L3XbcXGZSkQ+M7e+cv72Aggx8NvvGZTzfVfvHoAx2cKePdV6xq+Tts08OlbduBtf/9wTXFaCxEAukV5Tck6gmzbyn5YBpF5betD1ywRsXDB8j5Zxvvt67aAgOALDxyQI3oAdi0f/PB1gYZ42zTwqbftwCfetB3v+uJP8dTRGZy/PBiqetcHXxGImGjG6sE4rto4HCj11SIVZaXRNUPxKpFJCJGp9RqNRqM5t9CCbIG5Ydty3P6q83DLy9bgu0+dwN/dvx8A8OlbdqDMZ1XW2nV29aYRnLcsiZcmc4haBlJ8FM5liou1fdUAbnnZGjlGZ/1IAu9rsP0/HCNgmwZ2rh+C69G6PUG/cu1G7Fg7UNMVDHPR6gH89GPXBxLeG3HeKHOd1LT4VYNxbFvZj/NGg4JoWSqKH/7PV+EHu08hU3Swoj8WPh2++YFXIGKKkqWJ//W6C3D7q89DIuQmqbMpVWK2iS+892V48989iNWh/qxmg6tr8U+3XQGjydxEQgjWDSdww7axhvfTaDQazbkFCWdWLSV27txJd+3a1e1l1CVTrOD6v/wxLlo9gC+9b2fTIcd3PnIIn7x7D3b9wQ3oj9k4kytjKGEv6HDkXMkBIXMTHPOl7HgoOm6gV6zkuKi4tKWSZ6dIFyuItJA8v5DPF7PMQNCqRqPRaM5+CCFPUEp31rxNC7LOki5WkIxYLTVkU0oxmS03LXtpNBqNRqNZejQSZD31X3RCyI2EkL2EkP2EkI90ez0LQX/Mbnl3HCFEizGNRqPRaM5BekaQEUJMAJ8DcBOACwHcSgi5sLur0mg0Go1Go+k8PSPIAFwJYD+l9CVKaRnA1wHc3OU1aTQajUaj0XScXhJkqwEcVX4+xo8FIITcTgjZRQjZNTExsWiL02g0Go1Go+kUvSTIWoJS+kVK6U5K6c7R0dHmD9BoNBqNRqPpcXpJkB0HsFb5eQ0/ptFoNBqNRnNW00uC7HEAWwghGwkhEQDvAvDdLq9Jo9FoNBqNpuP0TFI/pdQhhPwWgP8EYAL4MqV0d5eXpdFoNBqNRtNxekaQAQCl9B4A93R7HRqNRqPRaDSLSS+VLDUajUaj0WjOSbQg02g0Go1Go+kyS3qWJSFkAsDhDj/NMgCTHX6OcwV9LRcGfR0XBn0dFwZ9HRcGfR0Xhl6/jusppTUzu5a0IFsMCCG76g0C1bSHvpYLg76OC4O+jguDvo4Lg76OC8NSvo66ZKnRaDQajUbTZbQg02g0Go1Go+kyWpA154vdXsBZhL6WC4O+jguDvo4Lg76OC4O+jgvDkr2OuodMo9FoNBqNpstoh0yj0Wg0Go2my2hB1gBCyI2EkL2EkP2EkI90ez1LCULIIULIs4SQpwghu/ixYULIfxFCXuRfh7q9zl6DEPJlQshpQshzyrGa140wPsvfn88QQi7v3sp7izrX8Y8IIcf5e/IpQsjrlds+yq/jXkLI67qz6t6DELKWEHI/IeR5QshuQsj/4Mf1e7INGlxH/Z5sA0JIjBDyGCHkaX4d/5gf30gIeZRfr3/l87BBCInyn/fz2zd0c/3N0IKsDoQQE8DnANwE4EIAtxJCLuzuqpYcr6WUXqpsQf4IgPsopVsA3Md/1gT5CoAbQ8fqXbebAGzhf24H8PlFWuNS4Cuovo4A8Nf8PXkpH9UG/vf6XQC288f8Pf/7rwEcAL9LKb0QwMsBfJBfL/2ebI961xHQ78l2KAG4jlJ6CYBLAdxICHk5gD8Hu46bAUwDeD+///sBTPPjf83v17NoQVafKwHsp5S+RCktA/g6gJu7vKalzs0A7uDf3wHgLV1cS09CKX0AwJnQ4XrX7WYAd1LGTwEMEkJWLs5Ke5s617EeNwP4OqW0RCk9CGA/2N//cx5K6UlK6c/49xkAewCshn5PtkWD61gP/Z6sAX9fZfmPNv9DAVwH4N/58fD7UbxP/x3A9YQQskjLbRstyOqzGsBR5edjaPwXSBOEAvghIeQJQsjt/NhySulJ/v0pAMu7s7QlR73rpt+j7fNbvJT2ZaVkrq9jC/Byz2UAHoV+T86Z0HUE9HuyLQghJiHkKQCnAfwXgAMAZiilDr+Leq3kdeS3zwIYWdwVt44WZJpOcS2l9HKwEsYHCSGvUm+kbHuv3uLbJvq6zYvPA9gEVuo4CeAvu7ucpQMhJAXgmwA+RClNq7fp92Tr1LiO+j3ZJpRSl1J6KYA1YK7h1i4vacHQgqw+xwGsVX5ew49pWoBSepx/PQ3gLrC/OOOifMG/nu7eCpcU9a6bfo+2AaV0nP9j7gH4R/glIH0dG0AIscFExFcppd/ih/V7sk1qXUf9npw7lNIZAPcDuBqsNG7xm9RrJa8jv30AwNQiL7VltCCrz+MAtvDdGxGwBsvvdnlNSwJCSJIQ0ie+B/DzAJ4Du37v43d7H4DvdGeFS4561+27AH6J72x7OYBZpYykCRHqZXor2HsSYNfxXXxH1kawhvTHFnt9vQjvt/kSgD2U0r9SbtLvyTaodx31e7I9CCGjhJBB/n0cwM+B9ePdD+AWfrfw+1G8T28B8CPaw+GrVvO7nJtQSh1CyG8B+E8AJoAvU0p3d3lZS4XlAO7ivZMWgK9RSn9ACHkcwDcIIe8HcBjAO7q4xp6EEPIvAF4DYBkh5BiATwD4M9S+bvcAeD1Yw28ewG2LvuAepc51fA0h5FKw8tohAL8OAJTS3YSQbwB4Hmw33AcppW431t2DXAPgvQCe5X07APAx6Pdku9S7jrfq92RbrARwB99xagD4BqX0e4SQ5wF8nRDySQBPgolf8K//TAjZD7bJ513dWHSr6KR+jUaj0Wg0mi6jS5YajUaj0Wg0XUYLMo1Go9FoNJouowWZRqPRaDQaTZfRgkyj0Wg0Go2my2hBptFoNBqNRtNltCDTaDRnDYQQlxDylPKn7QH2hJCdhJDPtvmYQ4SQZe0+l0aj0Qh07IVGozlrIIRkKaWpLjzvIQA7KaWTi/3cGo3m7EA7ZBqN5qyHO1h/QQh5lhDyGCFkMz/+C4SQ5wghTxNCHuDHXkMI+R7/fpgQ8m0+/PmnhJAd/PgIIeSHhJDdhJD/B4Aoz/WL/DmeIoR8gYdYajQaTUO0INNoNGcT8VDJ8p3KbbOU0osB/B2Az/BjfwjgdZTSSwC8ucb5/hjAk5TSHWDJ6nfy458A8CCldDvYrNZ1AEAI2QbgnQCu4QOQXQDvWdiXqNFozkb06CSNRnM2UeBCqBb/onz9a/79QwC+wsfUfKvGY64F8HYAoJT+iDtj/QBeBeBt/PjdhJBpfv/rAbwMwON8dFgc/uBtjUajqYsWZBqN5lyBhr+nlP4GIeQqAG8A8AQh5GXzfA4C4A5K6UfneR6NRnOOoUuWGo3mXOGdytdHAIAQsolS+iil9A8BTABYG3rMT8BLjoSQ1wCYpJSmATwA4N38+E0Ahvj97wNwCyFkjN82TAhZ37FXpNFozhq0Q6bRaM4m4oSQp5Sff0ApFdEXQ4SQZwCUANzKj32aELIFzNm6D8DTAF6tPP6PAHyZPy7//7dzxzQIBUEABd9iFAFYoEENHhCAA+jBBsWnQQLJETJTbnfdy16y1f4zP1XnmblV1+pZtW3bfWaO1WVmdtWrOlSP7z4T+DfOXgB/z1kK4Nf5sgQAWMyGDABgMRsyAIDFBBkAwGKCDABgMUEGALCYIAMAWEyQAQAs9gZAaI79kP3u0QAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 720x360 with 1 Axes>"
       ]
@@ -1108,7 +1106,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -1138,7 +1136,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -1203,7 +1201,7 @@
     "    next_state, reward, done, _ = env.step(action)\n",
     "    \n",
     "    interaction = {'state': state_array, 'action': action, 'next_state': next_state.copy(),\n",
-    "                   'reward': reward, 'done':float(done)}\n",
+    "                   'reward': reward, 'done':np.float32(done)}\n",
     "    \n",
     "    return interaction"
    ]
@@ -1456,9 +1454,9 @@
     "            training_batch = np.random.choice(replay_memory, size=batch_size)\n",
     "            Q_learning_update(np.asarray([x['state'] for x in training_batch]),\n",
     "                              np.asarray([x['action'] for x in training_batch]),\n",
-    "                              np.asarray([x['reward'] for x in training_batch], dtype=float),\n",
+    "                              np.asarray([x['reward'] for x in training_batch], dtype=np.float32),\n",
     "                              np.asarray([x['next_state'] for x in training_batch]),\n",
-    "                              np.asarray([x['done'] for x in training_batch], dtype=float),\n",
+    "                              np.asarray([x['done'] for x in training_batch], dtype=np.float32),\n",
     "                              model, gamma, n_actions)\n",
     "        \n",
     "        # Update target model\n",
@@ -1503,7 +1501,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 720x360 with 1 Axes>"
       ]
@@ -1569,7 +1567,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.10.9 (main, Dec  7 2022, 13:47:07) [GCC 12.2.0]"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
+   }
   }
  },
  "nbformat": 4,

From 4e7c22dedc2e231d7eab3dd4cd3f0235444e51e0 Mon Sep 17 00:00:00 2001
From: Jae Yoo <jaeyoo@google.com>
Date: Wed, 15 Mar 2023 18:19:59 -0700
Subject: [PATCH 10/13] Upgrade seaborn version to 0.12.0

---
 scripts/ci_validate_tutorials.sh | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/scripts/ci_validate_tutorials.sh b/scripts/ci_validate_tutorials.sh
index 8dcd5af27..c643f40fd 100755
--- a/scripts/ci_validate_tutorials.sh
+++ b/scripts/ci_validate_tutorials.sh
@@ -20,6 +20,8 @@ pip install jupyter nbclient==0.6.5 jupyter-client==6.1.12 ipython==7.22.0
 pip install ipykernel==5.1.1
 # OpenAI Gym pip package needed for the quantum reinforcement learning tutorial
 pip install gym==0.24.1
+# seaborn has also numpy dependency, it requires version >= 0.12.0.
+pip install seaborn=0.12.0
 # tf_docs pip package needed for noise tutorial.
 pip install -q git+https://github.com/tensorflow/docs
 # Leave the quantum directory, otherwise errors may occur

From a765f7ff84e2b7edb19e324b8d6a33171a7d5984 Mon Sep 17 00:00:00 2001
From: Jae Yoo <jaeyoo@google.com>
Date: Wed, 15 Mar 2023 19:28:20 -0700
Subject: [PATCH 11/13] Fix typo

---
 scripts/ci_validate_tutorials.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/ci_validate_tutorials.sh b/scripts/ci_validate_tutorials.sh
index c643f40fd..e58355faf 100755
--- a/scripts/ci_validate_tutorials.sh
+++ b/scripts/ci_validate_tutorials.sh
@@ -21,7 +21,7 @@ pip install ipykernel==5.1.1
 # OpenAI Gym pip package needed for the quantum reinforcement learning tutorial
 pip install gym==0.24.1
 # seaborn has also numpy dependency, it requires version >= 0.12.0.
-pip install seaborn=0.12.0
+pip install seaborn==0.12.0
 # tf_docs pip package needed for noise tutorial.
 pip install -q git+https://github.com/tensorflow/docs
 # Leave the quantum directory, otherwise errors may occur

From e70e7afa533068cc6e1d8620a3670e7f1ea12db4 Mon Sep 17 00:00:00 2001
From: Jae Yoo <jaeyoo@google.com>
Date: Wed, 15 Mar 2023 20:07:43 -0700
Subject: [PATCH 12/13] Remove debug print

---
 tensorflow_quantum/python/optimizers/spsa_minimizer.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/tensorflow_quantum/python/optimizers/spsa_minimizer.py b/tensorflow_quantum/python/optimizers/spsa_minimizer.py
index 28674ca20..4da5d0384 100644
--- a/tensorflow_quantum/python/optimizers/spsa_minimizer.py
+++ b/tensorflow_quantum/python/optimizers/spsa_minimizer.py
@@ -291,8 +291,6 @@ def _body(state):
 
             post_state = _spsa_once(SPSAOptimizerResults(**pre_state_params))[0]
             post_state_params = post_state.to_dict()
-            tf.print("asdf", state.objective_value.dtype,
-                     state.objective_value_prev.dtype)
             post_state_params.update({
                 "num_iterations":
                     post_state.num_iterations + 1,

From 1317edf584cf978a106ecb657faf3d3fdef67402 Mon Sep 17 00:00:00 2001
From: Jae Yoo <jaeyoo@google.com>
Date: Thu, 16 Mar 2023 11:42:42 -0700
Subject: [PATCH 13/13] Update ci_install bazel version, removed duplicated
 code lines.

---
 scripts/ci_install.sh                                  | 4 ++--
 tensorflow_quantum/python/optimizers/spsa_minimizer.py | 2 --
 2 files changed, 2 insertions(+), 4 deletions(-)

diff --git a/scripts/ci_install.sh b/scripts/ci_install.sh
index 99b3dc45e..a0ed81c8c 100755
--- a/scripts/ci_install.sh
+++ b/scripts/ci_install.sh
@@ -13,7 +13,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-wget https://github.com/bazelbuild/bazel/releases/download/3.7.2/bazel_3.7.2-linux-x86_64.deb
-sudo dpkg -i bazel_3.7.2-linux-x86_64.deb
+wget https://github.com/bazelbuild/bazel/releases/download/5.1.0/bazel_5.1.0-linux-x86_64.deb
+sudo dpkg -i bazel_5.1.0-linux-x86_64.deb
 pip install --upgrade pip setuptools wheel
 pip install -r requirements.txt
\ No newline at end of file
diff --git a/tensorflow_quantum/python/optimizers/spsa_minimizer.py b/tensorflow_quantum/python/optimizers/spsa_minimizer.py
index 4da5d0384..471b46d9e 100644
--- a/tensorflow_quantum/python/optimizers/spsa_minimizer.py
+++ b/tensorflow_quantum/python/optimizers/spsa_minimizer.py
@@ -263,8 +263,6 @@ def _spsa_once(state):
                     "objective_value": current_obj
                 })
 
-                return [SPSAOptimizerResults(**next_state_params)]
-
             return [SPSAOptimizerResults(**next_state_params)]
 
         # The `state` here is a `SPSAOptimizerResults` tuple with