docstr: Class for applying Layer Normalization over a mini-batch of inputs.
  
  Parameters
  ----------
  normalized_shape
      Trailing shape to applying the normalization to.
  epsilon
      small constant to add to the denominator,
      use global ivy.min_base by default.
  elementwise_affine
      Whether to include learnable affine parameters, default is ``True``.
  new_std
      The standard deviation of the new normalized values. Default is 1.
  device
      device on which to create the layer's variables 'cuda:0', 'cuda:1', 'cpu'
      etc. (Default value = None)
  v
      the variables for each submodule in the sequence,
      constructed internally by default.
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code: def __init__(
      self,
      normalized_shape,
      /,
      *,
      eps: float = 1e-05,
      elementwise_affine: bool = True,
      new_std: float = 1.0,
      device=None,
      v=None,
      dtype=None,
  ):
      """Class for applying Layer Normalization over a mini-batch of inputs.
  
      Parameters
      ----------
      normalized_shape
          Trailing shape to applying the normalization to.
      epsilon
          small constant to add to the denominator,
          use global ivy.min_base by default.
      elementwise_affine
          Whether to include learnable affine parameters, default is ``True``.
      new_std
          The standard deviation of the new normalized values. Default is 1.
      device
          device on which to create the layer's variables 'cuda:0', 'cuda:1', 'cpu'
          etc. (Default value = None)
      v
          the variables for each submodule in the sequence,
          constructed internally by default.
      """
      if isinstance(normalized_shape, int):
          normalized_shape = (normalized_shape,)
      self._normalized_idxs = [
          -(i + 1) for i in range(len(normalized_shape))
      ]
      self._epsilon = eps
      self._elementwise_affine = elementwise_affine
      self._new_std = new_std
      self._weight_shape = normalized_shape
      self._bias_shape = normalized_shape
      self._weight_init = Ones()
      self._bias_init = Zeros()
      Module.__init__(self, device=device, v=v, dtype=dtype)
  
file_path: ivy/ivy/stateful/norms.py
imports: import ivy
  from ivy.stateful.module import Module
  from ivy.stateful.initializers import Zeros, Ones
label_desc: Use image node_id 1 to create a new LayerNorm object from inherited base classes: Module with example: obj = LayerNorm()