code: def _validate_and_get_batch_size(
      flat_in_dims: List[Optional[int]], flat_args: List
  ) -> int:
      batch_sizes = [
          arg.size(in_dim)
          for in_dim, arg in zip(flat_in_dims, flat_args)
          if in_dim is not None
      ]
      if len(batch_sizes) == 0:
          raise ValueError(
              "vmap: Expected at least one Tensor to vmap over"
          )
      if batch_sizes and any(
          size != batch_sizes[0] for size in batch_sizes
      ):
          raise ValueError(
              f"vmap: Expected all tensors to have the same size in the mapped "
              f"dimension, got sizes {batch_sizes} for the mapped dimension"
          )
      return batch_sizes[0]
  
imports: import torch
  import functools
  import threading
  from torch import Tensor
  from typing import Any, Callable, Optional, Tuple, Union, List
  from torch.utils._pytree import tree_flatten, tree_unflatten, tree_map_, _broadcast_to_and_flatten, TreeSpec
  from functools import partial
  import os
  import itertools
  from torch._C._functorch import _add_batch_dim, _remove_batch_dim, _vmap_decrement_nesting, _vmap_increment_nesting, is_batchedtensor
label_desc: Use image node_id 2 for calling a global function with example usage: _validate_and_get_batch_size(flat_in_dims, flat_args) and returns: batch_sizes