code: def add_batch_dim(arg, bdim, batch_size=3):
      assert bdim == 0 or bdim == -1
      assert isinstance(arg, torch.Tensor)
      if bdim == 0:
          shape = [1] * len(arg.shape)
          shape.insert(bdim, batch_size)
          return (arg.repeat(shape), bdim)
      if bdim == -1:
          arg = (
              arg.unsqueeze(-1)
              .expand(*arg.shape, batch_size)
              .contiguous()
          )
          return (arg, bdim)
  
imports: import itertools
  import torch
  from functorch import vmap
  import torch.utils._pytree
  from functorch_additional_op_db import additional_op_db
  from torch.testing._internal.common_methods_invocations import DecorateInfo
  from torch.testing._internal.common_methods_invocations import op_db
  from torch.testing._internal.common_modules import module_db
  import os
  import unittest
  from torch.testing._internal.common_device_type import toleranceOverride
  from torch.testing._internal.autograd_function_db import autograd_function_db
  from collections import namedtuple
label_desc: Use image node_id 7 for calling a global function with example usage: add_batch_dim(arg, bdim, batch_size) and returns: arg,  bdim, arg,  bdim