code: def merge_batch(data: List[Any], batch_size_dim=0) -> Any:
      """Merge micro batches into a batch.
  
      Args:
          data (List[Any]): A list of micro batches.
  
      Returns:
          Any: Merge batch.
      """
      if len(data) == 0:
          return
      flattened_data = []
      tree_spec = None
      for d in data:
          # elems should be an instance of OrderedDict
          elems, tree_spec = tree_flatten_hf(d)
          flattened_data.append(elems)
      merged_data = []
  
      for elem_batch in zip(*flattened_data):
          if isinstance(elem_batch[0], torch.Tensor):
              if (
                  len(elem_batch[0].shape) == 0
              ):  # set loss to None in pipeline outputs
                  merged_data.append(None)
              else:
                  merged_data.append(
                      torch.cat(elem_batch, dim=batch_size_dim)
                  )
          else:
              merged_data.append(list(elem_batch))
      return tree_unflatten(merged_data, tree_spec)
  
file_path: colossalai/colossalai/pipeline/schedule/_utils.py
imports: from collections import OrderedDict
  from typing import Any, List, Optional, Tuple
  import torch
  import torch.cuda
  from torch.nn import Module
  from torch.utils._pytree import SUPPORTED_NODES, TreeSpec, _register_pytree_node, tree_flatten, tree_map, tree_unflatten
label_desc: Use image node_id 11 for calling a global function with example usage: merge_batch(data, batch_size_dim) and returns: tree_unflatten