code: def test_fromfile(
      tmpdir, np_dtype, torch_dtype, byte_order, count, mode
  ):
      path = tmpdir / "data.bin"
      rng = np.random.RandomState(0)
      rng.randn(5 if count == -1 else count + 1).astype(
          np_dtype
      ).tofile(path)
  
      for count_ in (-1, count // 2):
          expected = torch.from_numpy(
              np.fromfile(path, dtype=np_dtype, count=count_).astype(
                  np_dtype[1:]
              )
          )
  
          with open(path, mode) as file:
              actual = fromfile(
                  file,
                  dtype=torch_dtype,
                  byte_order=byte_order,
                  count=count_,
              )
  
          torch.testing.assert_close(actual, expected)
  
imports: import gzip
  import pathlib
  import sys
  import numpy
  import pytest
  import torch
  from datasets_utils import make_fake_flo_file, make_tar
  from torchdata.datapipes.iter import FileOpener, TarArchiveLoader
  from torchvision.datasets._optical_flow import _read_flo
  from torchvision.datasets.utils import _decompress
  from torchvision.prototype.datasets.utils import Dataset, GDriveResource, HttpResource, OnlineResource
  from torchvision.prototype.datasets.utils._internal import fromfile, read_flo
label_desc: Use image node_id 1 for calling a global function with example usage: test_fromfile(tmpdir, np_dtype, torch_dtype, byte_order, count, mode) without return types