code: def tensor(
      self,
      storage,
      shape,
      stride=None,
      *,
      storage_offset=None,
      dtype=None,
      requires_grad=None,
      is_leaf=None,
      **metadata,
  ):
      stride = _stride_or_default(stride, shape=shape)
      storage_offset = _storage_offset_or_default(storage_offset)
      dtype = _dtype_or_default(dtype)
      is_leaf = _is_leaf_or_default(is_leaf)
      requires_grad = _requires_grad_or_default(requires_grad)
      t = torch.tensor(
          [],
          dtype=dtype,
          device=storage.device,
          requires_grad=requires_grad,
      )
      with torch.no_grad():
          t.set_(storage, storage_offset, shape, stride)
      if not is_leaf:
          # Fake up some autograd history in a very naughty way
          with torch.enable_grad():
              t = t.clone(memory_format=torch.preserve_format)
          with torch.no_grad():
              t.set_(storage, storage_offset, shape, stride)
      assert torch._subclasses.meta_utils.safe_is_leaf(t) == is_leaf
      torch._utils.set_tensor_metadata(t, metadata)
      self.args.append(t)
      return t
  
imports: import copy
  import functools
  import getpass
  import itertools
  import logging
  import os
  import subprocess
  import tempfile
  import textwrap
  from collections import Counter
  from importlib import import_module
  from typing import Callable, Optional, TypeVar
  import torch
  import torch._prims_common
  import torch._subclasses.meta_utils
  from torch._dynamo.testing import rand_strided
  from torch._prims_common import is_float_dtype
  from torch.multiprocessing.reductions import StorageWeakRef
  from torch.utils._content_store import ContentStoreReader, ContentStoreWriter
  from .None import config
  from .utils import clone_inputs, get_debug_dir
label_desc: Use image node_id 3 for calling the InputReader obj's underlying member method code with example usage: obj.tensor(storage, shape, stride) and returns: t