code: def layer_norm(
      input, normalized_shape, weight=None, bias=None, eps=1e-05
  ):
      shape = ivy.shape(input)
      if (
          isinstance(normalized_shape, int)
          and normalized_shape == shape[-1]
      ):
          axis = [-1]
      else:
          assert ivy.all(
              ivy.equal(
                  normalized_shape, shape[-len(normalized_shape) :]
              )
          )
          axis = list(
              range(len(shape) - len(normalized_shape), len(shape))
          )
      return ivy.layer_norm(
          input, axis, scale=weight, offset=bias, eps=eps
      )
  
imports: import ivy
  from ivy.func_wrapper import with_unsupported_dtypes
  from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
label_desc: Use image node_id 4 for calling a global function with example usage: layer_norm(input, normalized_shape, weight, bias, eps) and returns: ivy