TF_Buffer* buf) {
  string content;
  cell->cell.value().SerializeToString(&content);
  void* data = tensorflow::port::Malloc(content.length());
  content.copy(static_cast<char*>(data), content.length(), 0);
  buf->data = data;
  buf->length = content.length();
  buf->data_deallocator = [](void* data, size_t length) {
    tensorflow::port::Free(data);
  };

      names, trademarks, service marks, or product names of the Licensor,
      except as required for reasonable and customary use in describing the
      origin of the Work and reproducing the content of the NOTICE file.

   7. Disclaimer of Warranty. Unless required by applicable law or
      agreed to in writing, Licensor provides the Work (and each
      Contributor provides its Contributions) on an "AS IS" BASIS,

  TFE_CancelCallback callback2;
  bool callback2_invoked = false;
  callback2.context = &callback2_invoked;
  callback2.callback = [](void* context) {
    *reinterpret_cast<bool*>(context) = true;
  };
  TFE_CancellationToken token2 = TFE_CancellationManagerGetToken(c_mgr);
  EXPECT_TRUE(TFE_CancellationManagerRegisterCallback(c_mgr, token2, &callback2,

   *     my_add2 = b + b
   *     return my_add1, my_add2
   *
   * Now we will execute this function with an eager context:
   *
   *   output1, output2 = two_adds(2.0, 3.0)
   *
   * and check that we got 5.0 and 6.0 as results.
   */

  // Build eager context.
  TFE_ContextOptions* opts = TFE_NewContextOptions();
  TFE_ContextOptionsSetTfrt(opts, std::get<1>(GetParam()));

      (TF_GetCode(status) != TF_OUT_OF_RANGE)) {
    return -1;
  }
  int64_t read;
  auto content_length = stream.headers().find("content-length");
  if (content_length == stream.headers().end()) {
    // When we read a file with offset that is bigger than the actual file size.
    // GCS will return an empty header (e.g no `content-length` header). In this
    // case, we will set read to `0` and continue.
    read = 0;

  Env* env = Env::Default();
  void* dso_handle;
  TF_RETURN_IF_ERROR(env->LoadDynamicLibrary(dso_path.c_str(), &dso_handle));

  // Step 2: Load symbol for `TF_InitPlugin`
  void* dso_symbol;
  TF_RETURN_WITH_CONTEXT_IF_ERROR(
      env->GetSymbolFromLibrary(dso_handle, "TF_InitPlugin", &dso_symbol),
      "Failed to load TF_InitPlugin symbol for DSO: ", dso_path);

  // Step 3: Call `TF_InitPlugin`
  TF_FilesystemPluginInfo info;

          // Expect a partial read.
          EXPECT_EQ(got.size(), size - offset)
              << "block size = " << block_size << ", offset = " << offset
              << ", n = " << n;
        }
        // Verify the contents of the read.
        std::vector<char>::const_iterator begin = buf.begin() + offset;
        std::vector<char>::const_iterator end =
            offset + n > buf.size() ? buf.end() : begin + n;

    }
    std::vector<MaybeParallelTensorOwned> result_content;
    result_content.reserve(1);
    result_content.push_back(ParallelTensor::FromTensorHandles(
        parallel_device, std::move(components), status));
    if (TF_GetCode(status) != TF_OK) return result;
    result.emplace(std::move(result_content));
    return result;
  } else if (operation_name == std::string("TPUReplicatedOutput")) {

// Returns the graph content in a human-readable format, with length set in
// `len`. The format is subject to change in the future.
// The returned string is heap-allocated, and caller should call free() on it.
TF_CAPI_EXPORT extern const char* TF_GraphDebugString(TF_Graph* graph,
                                                      size_t* len);

// Returns the function content in a human-readable format, with length set in

}

TF_CAPI_EXPORT extern void TFE_AbortCollectiveOps(TFE_Context* ctx,
                                                  TF_Status* status) {
  tensorflow::EagerContext* context =
      tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
  auto collective_executor_handle = context->GetCollectiveExecutorHandle();
  collective_executor_handle->get()->StartAbort(status->status);
}