class_docstr: Ghost Batch Normalization.
  It allows the use of large batch sizes,
  but with batch normalization parameters calculated on smaller sub-batches.
  
  [1] Train longer, generalize better: closing the generalization gap in large batch training of neural networks : https://arxiv.org/abs/1705.08741
  [2] Simple Modifications to Improve Tabular Neural Networks: https://arxiv.org/pdf/2108.03214
code: def forward(self, x):
      chunks = x.chunk(
          int(np.ceil(x.shape[0] / self.virtual_batch_size)), 0
      )
      res = [self.bn(x_) for x_ in chunks]
  
      return torch.cat(res, dim=0)
  
file_path: autogluon/multimodal/src/autogluon/multimodal/models/mlp.py
imports: from typing import Optional
  import numpy
  import torch
  from torch import nn
label_desc: Use image node_id 2 for calling the GhostBatchNorm obj's underlying member method code with example usage: obj.forward(x) and returns: torch