class_docstr: Applies layer normalization to the input tensor.
  
  Arguments
  ---------
  input_shape : tuple
      The expected shape of the input.
  eps : float
      This value is added to std deviation estimation to improve the numerical
      stability.
  elementwise_affine : bool
      If True, this module has learnable per-element affine parameters
      initialized to ones (for weights) and zeros (for biases).
  
  Example
  -------
  >>> input = torch.randn(100, 101, 128)
  >>> norm = LayerNorm(input_shape=input.shape)
  >>> output = norm(input)
  >>> output.shape
  torch.Size([100, 101, 128])
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code: def __init__(
      self,
      input_size=None,
      input_shape=None,
      eps=1e-05,
      elementwise_affine=True,
  ):
      super().__init__()
      self.eps = eps
      self.elementwise_affine = elementwise_affine
  
      if input_shape is not None:
          input_size = input_shape[2:]
  
      self.norm = torch.nn.LayerNorm(
          input_size,
          eps=self.eps,
          elementwise_affine=self.elementwise_affine,
      )
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 1 to create a new LayerNorm object from inherited base classes: nn with example: obj = LayerNorm(input_size, input_shape, eps, elementwise_affine)