tf_gcs_filesystem::RamFileBlockCache cache2(0, 1, 0, fetcher);
  tf_gcs_filesystem::RamFileBlockCache cache3(0, 0, 0, fetcher);
  tf_gcs_filesystem::RamFileBlockCache cache4(1000, 1000, 0, fetcher);
  std::vector<char> out;
  TF_EXPECT_OK(ReadCache(&cache1, want_filename, want_offset, want_n, &out));
  EXPECT_EQ(calls, 1);
  TF_EXPECT_OK(ReadCache(&cache2, want_filename, want_offset, want_n, &out));

          exempt-pr-labels: "override-stale"
          #Limit the No. of API calls in one run default value is 30.
          operations-per-run: 1000
          days-before-issue-stale: 7
          days-before-issue-close: 7
          stale-issue-label: "stale"
          # reason for closed the issue default value is not_planned
          close-issue-reason: completed

  struct stat sbuf;
  if (stat(path, &sbuf) != 0) {
    TF_SetStatusFromIOError(status, errno, path);
  } else {
    stats->length = sbuf.st_size;
    stats->mtime_nsec = sbuf.st_mtime * (1000 * 1000 * 1000);
    stats->is_directory = S_ISDIR(sbuf.st_mode);
    TF_SetStatus(status, TF_OK, "");
  }
}

static int GetChildren(const TF_Filesystem* filesystem, const char* path,

  EXPECT_EQ(0, TF_OperationNumControlOutputs(feed));
  EXPECT_EQ(0, TF_OperationGetControlOutputs(feed, control_ops, 100));

  EXPECT_EQ(0, TF_OperationNumControlInputs(neg));
  EXPECT_EQ(0, TF_OperationGetControlInputs(neg, control_ops, 100));
  EXPECT_EQ(0, TF_OperationNumControlOutputs(neg));
  EXPECT_EQ(0, TF_OperationGetControlOutputs(neg, control_ops, 100));

  {
    std::unique_ptr<ParallelTensor> float_tensors =
        parallel_device.ScalarsFromSequence<float>({10.0, 11.0}, context.get(),
                                                   status.get());
    ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());
    ExpectScalarEq<float>(float_tensors->tensor(0), 10.0);
    ExpectScalarEq<float>(float_tensors->tensor(1), 11.0);
  }

  {

   
   | TF  | CUDA |
   
   | :-------------: | :-------------: |
   
   | 2.5.0  | 11.2 |
   
   | 2.4.0  | 11.0 |
   
   | 2.1.0 - 2.3.0  | 10.1 |
   
   | 1.13.1 - 2.0  | 10.0  |
   
   | 1.5.0 - 1.12.0 | 9.0 |
   
     * If you have above configuration and using _**Windows**_ platform -
       * Try adding the CUDA, CUPTI, and cuDNN installation directories to the %PATH% environment variable.

    *   The `TextVectorization` layer now accounts for the mask_token as part of
        the vocabulary size when output_mode='int'. This means that, if you have
        a max_tokens value of 5000, your output will have 5000 unique values
        (not 5001 as before).
    *   Change the return value of `TextVectorization.get_vocabulary()` from
        `byte` to `string`. Users who previously were calling 'decode' on the