class_docstr: Conversion class for LayerNorm.
code: def from_observed(cls, observed_module: _nn.Module):
      cls.do_attribute_assertions(observed_module)
      finalized_weights = cls.get_finalized_weights(observed_module)
      return_module = _nn.LayerNorm(
          normalized_shape=observed_module.normalized_shape,
          eps=observed_module.eps,
          elementwise_affine=observed_module.elementwise_affine,
          device=observed_module.device
          if hasattr(observed_module, "device")
          else None,
          dtype=observed_module.dtype
          if hasattr(observed_module, "dtype")
          else None,
      )
      if observed_module.elementwise_affine:
          return_module.weight = _nn.Parameter(finalized_weights)
          if observed_module.bias:
              return_module.bias = _nn.Parameter(
                  observed_module.bias.detach()
              )
      return_module.activation_post_process = (
          observed_module.activation_post_process
      )
      return return_module
  
imports: import torch.nn
  import torch.nn.qat
  from ._supported_modules import Conv1d, Embedding, LayerNorm, MultiheadAttention
label_desc: Use image node_id 2 for calling the LayerNormPalettizationConversion obj's underlying member method code with example usage: obj.from_observed(cls, observed_module) and returns: return_module