class_docstr: BatchNorm2d or Identity layer if the batch_size is 1.
  
  Workaround for: https://github.com/pytorch/pytorch/issues/4534
class_docstr_tok: ["BatchNorm2d","or","Identity","layer","if","the","batch_size","is","1",".","Workaround","for",":","https",":","\/\/github.com\/pytorch\/pytorch\/issues\/4534"]
code: def forward(self, input: torch.Tensor) -> torch.Tensor:
      if input.shape[0] == 1:
          logger.warning(
              "Batch size is 1, but batch normalization requires batch size >= 2. Skipping batch normalization."
              "Make sure to set `batch_size` to a value greater than 1."
          )
          return input
      return super().forward(input)
  
imports: import logging
  from typing import Optional
  import numpy
  import torch
  from torch.nn import BatchNorm1d, BatchNorm2d, LayerNorm, Module
  from ludwig.utils.torch_utils import LudwigModule
label_desc: Use image node_id 1 for calling the BatchNorm2dOrIdentity obj's underlying member method code with example usage: obj.forward(input) and returns: super, input