code: def get_norm_layer(
      normalization: str = "layer_norm",
      axis: int = -1,
      epsilon: float = 1e-5,
      in_channels: int = 0,
      **kwargs,
  ):
      """Get the normalization layer based on the provided type
  
      Parameters
      ----------
      normalization
          The type of the layer normalization from ['layer_norm']
      axis
          The axis to normalize the
      epsilon
          The epsilon of the normalization layer
      in_channels
          Input channel
  
      Returns
      -------
      norm_layer
          The layer normalization layer
      """
      if isinstance(normalization, str):
          if normalization == "layer_norm":
              assert in_channels > 0
              assert axis == -1
              norm_layer = nn.LayerNorm(
                  normalized_shape=in_channels, eps=epsilon, **kwargs
              )
          else:
              raise NotImplementedError(
                  "normalization={} is not supported".format(
                      normalization
                  )
              )
          return norm_layer
      else:
          raise NotImplementedError(
              "The type of normalization must be str"
          )
  
imports: import math
  from collections import OrderedDict
  import torch
  import numpy
  from torch import nn
  from typing import Union
  from .utils import to_torch_dtype
label_desc: Use image node_id 4 for calling a global function with example usage: get_norm_layer(normalization, axis, epsilon, in_channels) and returns: norm_layer