class_docstr: Layer Normalization for colossalai
  
  Args:
      normalized_shape (int): input shape from an expected input of size.
          :math:`[* \times \text{normalized_shape}[0] \times \text{normalized_shape}[1]
          \times \ldots \times \text{normalized_shape}[-1]]`
          If a single integer is used, it is treated as a singleton list, and this module will
          normalize over the last dimension which is expected to be of that specific size.
      eps (float): a value added to the denominator for numerical stability, defaults to 1e-05.
      bias (bool, optional): Whether to add a bias, defaults to ``True``.
      dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
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code: def forward(self, x: Tensor) -> Tensor:
      return F.layer_norm(
          x,
          self.normalized_shape,
          self.weight,
          self.bias,
          self.variance_epsilon,
      )
  
imports: import math
  from typing import Callable
  import torch
  import torch.nn.functional
  from torch import Tensor
  from torch import nn
  from torch.nn.parameter import Parameter
  from colossalai.legacy.context import seed
  from colossalai.legacy.registry import LAYERS
  from colossalai.nn import init
  from colossalai.utils.device import get_current_device
  from ..utils import to_2tuple
label_desc: Use image node_id 2 for calling the VanillaLayerNorm obj's underlying member method code with example usage: obj.forward(x) and returns: F