code: def collate_error(e: Exception, batch):
      "Raises error when the batch could not collate, stating what items in the batch are different sizes and their types"
      err = f"Error when trying to collate the data into batches with fa_collate, at least two tensors in the batch are not the same size.\n\n"
      # we need to iterate through the entire batch and find a mismatch
      length = len(batch[0])
      for idx in range(length):  # for each type in the batch
          for i, item in enumerate(batch):
              if i == 0:
                  shape_a, type_a = (
                      item[idx].shape,
                      item[idx].__class__.__name__,
                  )
              elif item[idx].shape != shape_a:
                  shape_b = item[idx].shape
                  if shape_a != shape_b:
                      err += f"Mismatch found on axis {idx} of the batch and is of type `{type_a}`:\n\tItem at index 0 has shape: {shape_a}\n\tItem at index {i} has shape: {shape_b}\n\nPlease include a transform in `after_item` that ensures all data of type {type_a} is the same size"
                      e.args = [err]
                      raise
  
imports: from __future__ import annotations
  from ..torch_basics import *
  from torch.utils.data.dataloader import _MultiProcessingDataLoaderIter, _SingleProcessDataLoaderIter, _DatasetKind
label_desc: Use image node_id 4 for calling a global function with example usage: collate_error(e, batch) without return types