: std::fstream(temp_file_name, mode), name_(temp_file_name) {}

TempFile::TempFile(TempFile&& rhs)
    : std::fstream(std::move(rhs)), name_(std::move(rhs.name_)) {}

TempFile::~TempFile() {
  std::fstream::close();
  std::remove(name_.c_str());
}

const std::string TempFile::getName() const { return name_; }

bool TempFile::truncate() {
  std::fstream::close();

namespace {

#if !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD)

// Creates a placeholder representing an input to the cond or body graph.
// TODO(skyewm): remove these from final graph
bool CreateInput(const TF_Output& parent_input, TF_Graph* g, const char* name,
                 TF_Output* input, TF_Status* status) {

  // Check that copying it to the old remote device (named localhost) fails.
  TFE_TensorHandleCopyToDevice(h0_task0_new, ctx, remote_device_name, status);
  EXPECT_NE(TF_OK, TF_GetCode(status)) << TF_Message(status);

  // Copying and executing on the new remote device works.
  const char new_remote_device_name[] =
      "/job:worker/replica:0/task:1/device:CPU:0";

      immediate_execution_ctx_.get(), {x.get()}, UseFunction()));
}

TEST_P(CppGradients, TestMatMulGrad) {
  // TODO(vnvo2409): Figure out why `gradient_checker` does not work very
  // well with `MatMul` and remove `TestMatMul*` in
  // `mnist_gradients_test` when done.
  GTEST_SKIP();

  float A_vals[] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f};
  int64_t A_dims[] = {3, 3};
  AbstractTensorHandlePtr A;
  {

A. Unique TensorFlower <******@****.***> 1682633218 -0700

     *
     *    dA = U
     *    dbias = reduceSum(U, dims = channel_dim)
     */

    AbstractTensorHandle* upstream_grad = grad_outputs[0];
    DCHECK(upstream_grad);

    // Recover data format from forward pass for gradient.
    std::string data_format;
    TF_RETURN_IF_ERROR(forward_attrs_.Get("data_format", &data_format));

    // Grad for A
    grad_inputs[0] = upstream_grad;

  block_map_.emplace(std::make_pair(key, new_entry));
  return new_entry;
}

// Remove blocks from the cache until we do not exceed our maximum size.
void RamFileBlockCache::Trim() {
  while (!lru_list_.empty() && cache_size_ > max_bytes_) {
    RemoveBlock(block_map_.find(lru_list_.back()));
  }
}

/// Move the block to the front of the LRU list if it isn't already there.
void RamFileBlockCache::UpdateLRU(const Key& key,

void TestRemoteExecuteSilentCopiesOp(bool async, bool remote,
                                     bool remote_func_outputs = false) {
  return TestRemoteExecuteSilentCopies(async, remote, /*func=*/false,
                                       /*heavy_load_on_streaming_rpc=*/false,
                                       remote_func_outputs);
}

TEST(CAPI, RemoteExecuteSilentCopies) {

  EXPECT_EQ(out, A);
  TF_EXPECT_OK(ReadCache(&cache, "b", 0, n, &out));
  EXPECT_EQ(out, b);
  TF_EXPECT_OK(ReadCache(&cache, "b", 8, n, &out));
  EXPECT_EQ(out, B);
  EXPECT_EQ(calls, 4);
  // Remove the blocks from "a".
  cache.RemoveFile("a");
  // Both blocks from "b" should still be there.
  TF_EXPECT_OK(ReadCache(&cache, "b", 0, n, &out));
  EXPECT_EQ(out, b);
  TF_EXPECT_OK(ReadCache(&cache, "b", 8, n, &out));

      tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
  auto collective_executor_handle = context->GetCollectiveExecutorHandle();
  tensorflow::Notification done;
  collective_executor_handle->get()->remote_access()->CheckPeerHealth(
      task, timeout_in_ms, [&done, status](const Status& s) {
        status->status = s;
        done.Notify();
      });
  done.WaitForNotification();
}