[quantization] Introduce example script for torch.nn.LayerNorm quantization

[dvsav]

What

This PR introduces an example script quantize_layernorm.py that tests QuantLayerNorm wrapper for torch.nn.LayerNorm module.

Why

Traditinally, every QuantXXX wrapper in tico/quantization/wrapq/wrappers has a respective example script in tico/quantization/wrapq/examples that demostrates quantization of a respective module, estimation of quantization error and conversion to Circle. For some reason torch.nn.LayerNorm doesn't have such a script, and this PR is intended to fill this gap.

Script Output

$ python tico/quantization/wrapq/examples/nn/quantize_layernorm.py
Input shape:              torch.Size([10, 768])
Output shape (FP32):      torch.Size([10, 768])
Output shape (Quantized): torch.Size([10, 768])
┌───────────── Quantization Error Summary ─────────────
│ Mean |diff|: 0.012465
│ PEIR       : 0.598103 %
└──────────────────────────────────────────────────────
    ┌────────────────────────────────────────────┐
 4.1┤                                            │
    │                                         •  │
    │                                     •••    │
 2.7┤                                   •••      │
    │                                ••••        │
    │                              •••           │
 1.3┤                           ••••             │
    │                         ••••               │
    │                       •••                  │
-0.1┤                    ••••                    │
    │                  •••                       │
    │               ••••                         │
-1.5┤             ••••                           │
    │           •••                              │
    │         •••                                │
-2.9┤      •••                                   │
    │    •••                                     │
    │  •                                         │
-4.3┤                                            │
    └┬──────────┬──────────┬─────────┬──────────┬┘
   -4.3       -2.2       -0.1       2.0       4.1 

Circle model saved as 'quantized_layernorm.circle'

Note: the above output depens on PR [quantization] Fix a bug in AffineObserverBase.compute_qparams.

[mhs4670go]

LGTM