code: def forward(self, inputs):
      batch_size = inputs.shape[0]
  
      if self.training and self.virtual_batch_size:
          splits = inputs.chunk(
              int(np.ceil(batch_size / self.virtual_batch_size)), 0
          )
  
          if batch_size % self.virtual_batch_size == 1:
              # Skip batch normalization for the last chunk if it is size 1.
              logger.warning(
                  f"Virtual batch size `{self.virtual_batch_size}` is not a factor of the batch size `{batch_size}`, "
                  "resulting in a chunk of size 1. Skipping batch normalization for the last chunk of size 1."
              )
  
          if batch_size == 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."
              )
              # We temporarily set the batch_norm module to eval mode as we can't compute the running statistics
              # when the batch size is 1.
              self.bn.eval()
              splits_with_bn = [
                  self.bn(x) if x.shape[0] >= 1 else x for x in splits
              ]
              self.bn.train()
          else:
              splits_with_bn = [
                  self.bn(x) if x.shape[0] > 1 else x for x in splits
              ]
  
          return torch.cat(splits_with_bn, 0)
  
      if batch_size != 1 or not self.training:
          return self.bn(inputs)
      return inputs
  
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 2 for calling the GhostBatchNormalization obj's underlying member method code with example usage: obj.forward(inputs) and returns: inputs, torch, self