if (n == 0) {
    TF_SetStatus(status, TF_OK, "");
    return 0;
  }
  if (!IsCacheEnabled() || (n > max_bytes_)) {
    // The cache is effectively disabled, so we pass the read through to the
    // fetcher without breaking it up into blocks.
    return block_fetcher_(filename, offset, n, buffer, status);
  }
  // Calculate the block-aligned start and end of the read.

}
TEST(CAPI, ExecuteAdd) {
  ExecuteAdd(
      /*async=*/false,
      /*forward_input*/ false,
      /*tfrt*/ false);
}
// TODO(b/234067483): Investigate flakiness and re-enable.
TEST(CAPI, DISABLED_ExecuteAddAsync) {
  ExecuteAdd(
      /*async=*/true,
      /*forward_input*/ false,
      /*tfrt*/ false);
}
TEST(CAPI, ExecuteAddForward) {
  ExecuteAdd(
      /*async=*/false,

  int device_id = TFE_TensorHandleDeviceID(hcpu, status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());
  ASSERT_EQ(0, device_id) << device_id;

  // Disable the test if no GPU is present.
  string gpu_device_name;
  if (GetDeviceName(ctx, &gpu_device_name, "GPU")) {
    TFE_TensorHandle* hgpu = TFE_TensorHandleCopyToDevice(

  config_info_line('v1', 'Build with TensorFlow 1 API instead of TF 2 API.')

  print('Preconfigured Bazel build configs to DISABLE default on features:')
  config_info_line('nogcp', 'Disable GCP support.')
  config_info_line('nonccl', 'Disable NVIDIA NCCL support.')


if __name__ == '__main__':

    ```bash
    bazel test ${flags} //tensorflow/python/saved_model:load_test --test_filter=*LoadTest.test_capture_variables*
    ```

    **Note:** You can add `--test_sharding_strategy=disabled` to the `flags` to
    disable the sharding so that all the test outputs are in one file. However,
    it may slow down the tests for not running in parallel and may cause the

}

// All the *Helper methods are used as a workaround for the restrictions that
// one cannot call ASSERT_* methods in non-void-returning functions (when
// exceptions are disabled during compilation)
void PlaceholderHelper(TF_Graph* graph, TF_Status* s, const char* name,
                       TF_DataType dtype, const std::vector<int64_t>& dims,
                       TF_Operation** op) {

      pruning_thread_.reset(
          TF_StartThread(&thread_options, "TF_prune_FBC", PruneThread, this));
    }
    TF_VLog(1, "GCS file block cache is %s.\n",
            (IsCacheEnabled() ? "enabled" : "disabled"));
  }

  ~RamFileBlockCache() {
    if (pruning_thread_) {
      stop_pruning_thread_.Notify();
      // Destroying pruning_thread_ will block until Prune() receives the above
      // notification and returns.

        *   For older CPUs, oneDNN optimizations are disabled by default.
    *   **Windows x86 package:** oneDNN optimizations are disabled by default.
    *   **Linux aach64 (`--config=mkl_aarch64`) package:**
        *   Experimental oneDNN optimizations are disabled by default.
        *   If you experience issues with oneDNN optimizations on, we recommend

TEST(CAPI, DistributedFunctionNoError) {
  TestDistributedFunctionCancellation(false);
}

// TODO(b/170399182): Update test once an alternative to using the function
// optimization hook is in place.
TEST(CAPI, DISABLED_DistributedFunctionCancelledOnError) {
  TestDistributedFunctionCancellation(true);
}

void TestRemoteExecuteDeleteContextWithOutstandingRPC(bool async) {
  tensorflow::ServerDef server_def = GetServerDef(2);

# buildifier: disable=load-on-top

workspace(name = "wheel_test")

# buildifier: disable=load-on-top

load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")

http_archive(
    name = "bazel_skylib",
    sha256 = "74d544d96f4a5bb630d465ca8bbcfe231e3594e5aae57e1edbf17a6eb3ca2506",
    urls = [
        "https://storage.googleapis.com/mirror.tensorflow.org/github.com/bazelbuild/bazel-skylib/releases/download/1.3.0/bazel-skylib-1.3.0.tar.gz",