TFE_DeleteContext(ctx);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TF_DeleteStatus(status);
}
BENCHMARK(BM_InitOp);

void BM_Execute(::testing::benchmark::State& state) {
  const int async = state.range(0);
  state.SetLabel(async ? "ExecuteAsync" : "Execute");
  TF_Status* status = TF_NewStatus();
  TFE_ContextOptions* opts = TFE_NewContextOptions();

  int32_t init_timeout_in_ms = 300000;
  TFE_Context* ctx_0 =
      CreateContext(serialized_server_def_0,
                    /*isolate_session_state=*/false, init_timeout_in_ms);
  TFE_Context* ctx_1 =
      CreateContext(serialized_server_def_1,
                    /*isolate_session_state=*/false, init_timeout_in_ms);

  // Remote device on `worker2`.
  const char remote_device[] = "/job:localhost/replica:0/task:2/device:CPU:0";

  // When `reset_context` is true, initialize new cluster context state based
  // on cluster configurations provided in `server_def`; otherwise, update
  // existing context state with the provided `server_def`. Contexts created
  // on remote tasks will be considered stale and garbage collected after
  // `keep_alive_secs` of inactivity.

absl::Status ResetPjRtClient(const DeviceType& device_type) {
  ResourceMgr* rmgr = tfrt_global::GetTFGlobalResourceMgr();
  PjRtState* pjrt_state;
  TF_RETURN_IF_ERROR(rmgr->Lookup(rmgr->default_container(),
                                  kPjRtStateResourceName, &pjrt_state));
  TF_RETURN_IF_ERROR(pjrt_state->MovePjRtClientToUnused(device_type));
  return absl::OkStatus();
}

      new GradientTape<Gradient, BackwardFunction, TapeTensor>(false));
  AccumulatorCallState& call_state = call_state_.top();
  call_state.backward_tape = tape.get();
  auto pop_backward_tape =
      gtl::MakeCleanup([&call_state] { call_state.backward_tape = nullptr; });
  std::vector<Gradient*> forwardprop_aids;
  std::vector<int64_t> sources;
  std::unordered_set<int64_t> sources_set;

- Provide links to your TensorFlow model and (optionally) TensorFlow Lite Model.
```

### 3. Failure after conversion
If the conversion is successful, but the generated model is wrong, then state what is wrong:

- Model produces wrong results and/or has lesser accuracy.
- Model produces correct results, but it is slower than expected.

### 4. (optional) RNN conversion support

 public:
  using GradientTape<AbstractTensorHandle, GradientFunction,
                     TapeTensor>::GradientTape;
  // Returns whether the tape is persistent, i.e., whether the tape will hold
  // onto its internal state after a call to `ComputeGradient`.
  using GradientTape<AbstractTensorHandle, GradientFunction,
                     TapeTensor>::IsPersistent;

  // Adds this tensor to the list of watched tensors.
  //

        "//tensorflow/compiler/jit:pjrt_tensor_buffer_util",
        "//tensorflow/core:framework",
        "//tensorflow/core/tfrt/common:async_value_tensor",
        "//tensorflow/core/tfrt/common:global_state",
        "//tensorflow/core/tfrt/common:pjrt_state",
        "//tensorflow/core/tfrt/common:pjrt_util",
        "@com_google_absl//absl/status",
        "@com_google_absl//absl/status:statusor",
        "@com_google_absl//absl/strings",

    {
      tensorflow::mutex_lock l(execution_mutex_);
      while (execution_state_ == ExecutionState::kIdle ||
             execution_state_ == ExecutionState::kHasResult) {
        start_execute_.wait(l);
      }
      if (execution_state_ == ExecutionState::kShuttingDown) {
        return;
      } else if (execution_state_ == ExecutionState::kReadyToExecute) {
        // op_outputs_ may have been std::moved

    GTEST_SKIP() << "NewWritableFile() not supported: " << status;

  FileStatistics stat;
  status = env_->Stat(filepath, &stat);
  EXPECT_PRED2(UnimplementedOrReturnsCode, status, Code::OK);
  if (!status.ok()) GTEST_SKIP() << "Stat() not supported: " << status;
  EXPECT_FALSE(stat.is_directory);
  EXPECT_EQ(stat.length, 0);
}

TEST_P(ModularFileSystemTest, TestStatNonEmptyFile) {