code: def get_transform_func(num_tensors, dtype, device, is_fastpath):
      def transform(t):
          if not torch.is_tensor(t):
              return t
          if torch.is_tensor(t) and t.ndim == 0:
              return t
          return make_tensor(
              (num_tensors, num_tensors),
              dtype=dtype,
              device=device,
              requires_grad=True,
              noncontiguous=not is_fastpath,
          )
  
      return transform
  
imports: from contextlib import nullcontext
  from numbers import Number
  import random
  import re
  import torch
  import unittest
  import itertools
  import weakref
  from torch.testing import make_tensor
  from torch.testing._comparison import default_tolerances
  from torch.testing._internal.common_cuda import TEST_MULTIGPU
  from torch.testing._internal.common_utils import TestCase, run_tests, TEST_WITH_ROCM, skipIfTorchDynamo, parametrize, gradcheck
  from torch.testing._internal.common_device_type import instantiate_device_type_tests, dtypes, onlyCUDA, ops, OpDTypes
  from torch.testing._internal.common_methods_invocations import foreach_unary_op_db, foreach_binary_op_db, foreach_pointwise_op_db, foreach_reduce_op_db, foreach_other_op_db
  from torch.testing._internal.common_dtype import all_types_and_complex_and, floating_types_and, floating_types, integral_types_and
label_desc: Use image node_id 1 for calling a global function with example usage: get_transform_func(num_tensors, dtype, device, is_fastpath) and returns: transform, make_tensor, t, t