docstr: ivy.Array instance method variant of ivy.layer_norm. This method
  simply wraps the function, and so the docstring for ivy.layer_norm also
  applies to this method with minimal changes.
  
  Parameters
  ----------
  self
      Input array
  normalized_idxs
      Indices to apply the normalization to.
  scale
      Learnable gamma variables for elementwise post-multiplication,
      default is ``None``.
  offset
      Learnable beta variables for elementwise post-addition, default is ``None``.
  eps
      small constant to add to the denominator. Default is ``1e-05``.
  new_std
      The standard deviation of the new normalized values. Default is 1.
  out
      optional output array, for writing the result to. It must have a shape that
      the inputs broadcast to.
  
  Returns
  -------
  ret
      The layer after applying layer normalization.
  
  Examples
  --------
  >>> x = ivy.array([[0.0976, -0.3452,  1.2740],
  ...                   [0.1047,  0.5886,  1.2732],
  ...                   [0.7696, -1.7024, -2.2518]])
  >>> norm = x.layer_norm([0, 1], eps=0.001,
  ...                     new_std=1.5, scale=0.5, offset=[0.5, 0.02, 0.1])
  >>> print(norm)
  ivy.array([[ 0.826, -0.178, 0.981 ],
             [ 0.831,  0.421, 0.981 ],
             [ 1.26 , -1.05 , -1.28 ]])
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code: def layer_norm(
      self: ivy.Array,
      normalized_idxs: List[int],
      /,
      *,
      scale: Optional[Union[ivy.Array, ivy.NativeArray]] = None,
      offset: Optional[Union[ivy.Array, ivy.NativeArray]] = None,
      eps: float = 1e-05,
      new_std: float = 1.0,
      out: Optional[ivy.Array] = None,
  ) -> ivy.Array:
      """ivy.Array instance method variant of ivy.layer_norm. This method
      simply wraps the function, and so the docstring for ivy.layer_norm also
      applies to this method with minimal changes.
  
      Parameters
      ----------
      self
          Input array
      normalized_idxs
          Indices to apply the normalization to.
      scale
          Learnable gamma variables for elementwise post-multiplication,
          default is ``None``.
      offset
          Learnable beta variables for elementwise post-addition, default is ``None``.
      eps
          small constant to add to the denominator. Default is ``1e-05``.
      new_std
          The standard deviation of the new normalized values. Default is 1.
      out
          optional output array, for writing the result to. It must have a shape that
          the inputs broadcast to.
  
      Returns
      -------
      ret
          The layer after applying layer normalization.
  
      Examples
      --------
      >>> x = ivy.array([[0.0976, -0.3452,  1.2740],
      ...                   [0.1047,  0.5886,  1.2732],
      ...                   [0.7696, -1.7024, -2.2518]])
      >>> norm = x.layer_norm([0, 1], eps=0.001,
      ...                     new_std=1.5, scale=0.5, offset=[0.5, 0.02, 0.1])
      >>> print(norm)
      ivy.array([[ 0.826, -0.178, 0.981 ],
                 [ 0.831,  0.421, 0.981 ],
                 [ 1.26 , -1.05 , -1.28 ]])
      """
      return ivy.layer_norm(
          self,
          normalized_idxs,
          scale=scale,
          offset=offset,
          eps=eps,
          new_std=new_std,
          out=out,
      )
  
imports: from typing import Optional, List, Union
  import abc
  import ivy
label_desc: Use image node_id 1 for calling the _ArrayWithNorms obj's underlying member method code with example usage: obj.layer_norm() and returns: ivy