code: def scatter(*inputs, chunks: int) -> List[Batch]:
      """Splits an input mini-batch into multiple micro-batches."""
      if len(inputs) == 1 and isinstance(inputs[0], Tensor):
          return [Batch(x) for x in inputs[0].chunk(chunks)]
  
      batches: List[Any] = [[] for _ in range(chunks)]
      # Actual number of chunks produced
      num_chunks = -1
      for input in inputs:
          if torch.is_tensor(input):
              # Chunk only tensors.
              tensors = input.chunk(chunks)
  
              # Validate number of chunks equal across all inputs.
              if num_chunks != -1 and num_chunks != len(tensors):
                  raise RuntimeError(
                      f"Found different number of chunks produced for inputs: {num_chunks} and {len(tensors)}"
                  )
              num_chunks = len(tensors)
  
              for i, tensor in enumerate(tensors):
                  batches[i].append(tensor)
          else:
              # Replicate non-tensors or tensors wrapped with 'NoChunk'.
              for i in range(chunks):
                  if isinstance(input, NoChunk):
                      # Extract the tensor out.
                      batches[i].append(input.tensor)
                  else:
                      batches[i].append(input)
  
      # Truncate to actual number of chunks
      batches = batches[:num_chunks]
  
      return [Batch(x) for x in batches]
  
imports: import typing
  from typing import Any, Callable, List, Union, cast, Sequence
  import torch
  from torch import Tensor
  import torch.cuda.comm
label_desc: Use image node_id 2 for calling a global function with example usage: scatter() and returns: unknown, unknown