typedef struct TFE_MonitoringBoolGaugeCell TFE_MonitoringBoolGaugeCell;
TF_CAPI_EXPORT extern void TFE_MonitoringBoolGaugeCellSet(
    TFE_MonitoringBoolGaugeCell* cell, bool value);
TF_CAPI_EXPORT extern bool TFE_MonitoringBoolGaugeCellValue(
    TFE_MonitoringBoolGaugeCell* cell);

// APIs for Bool Gauge without label.
typedef struct TFE_MonitoringBoolGauge0 TFE_MonitoringBoolGauge0;
TF_CAPI_EXPORT extern TFE_MonitoringBoolGauge0* TFE_MonitoringNewBoolGauge0(

  TFE_CallDLManagedTensorDeleter(dlmt_vptr);
}

// Checks whether the stride array matches the layout of compact, row-majored
// data.
bool IsValidStrideCompactRowMajorData(int64_t* shape_arr, int64_t* stride_arr,
                                      int ndim) {
  bool valid = true;
  int64_t expected_stride = 1;
  for (int i = ndim - 1; i >= 0; --i) {
    // Empty tensors are always compact regardless of strides.

                       ForwardOperation* forward_op_) {
  std::unique_ptr<bool[]> b(new bool[num_values]);
  for (int i = 0; i < num_values; ++i) {
    b[i] = values[i];
  }
  forward_op_->attrs.Set(attr_name,
                         gtl::ArraySlice<const bool>(b.get(), num_values));
  return op_->SetAttrBoolList(attr_name, values, num_values);
}

                         int num_values) override {
    std::unique_ptr<bool[]> b(new bool[num_values]);
    for (int i = 0; i < num_values; ++i) {
      b[i] = values[i];
    }
    op_->node_builder.Attr(attr_name,
                           ArraySlice<const bool>(b.get(), num_values));

    return absl::OkStatus();
  }

}

TFE_Executor* TFE_NewExecutor(bool is_async, bool enable_streaming_enqueue,
                              int in_flight_nodes_limit) {
  return new TFE_Executor(is_async, enable_streaming_enqueue,
                          in_flight_nodes_limit);
}

void TFE_DeleteExecutor(TFE_Executor* executor) { delete executor; }

bool TFE_ExecutorIsAsync(TFE_Executor* executor) {

  explicit GradientTape(bool persistent) : persistent_(persistent) {}
  ~GradientTape() {
    for (const auto& pair : op_tape_) {
      pair.second.backward_function_deleter(pair.second.backward_function);
    }
  }

  // Returns whether any tensor in a list of tensors is being watched and has
  // a trainable dtype.
  bool ShouldRecord(absl::Span<const int64_t> tensor_ids,

// Set server def with timeout.
TF_CAPI_EXPORT extern void TFE_ContextSetServerDefWithTimeout(
    TFE_Context* ctx, int keep_alive_secs, const void* proto, size_t proto_len,
    int64_t init_timeout_in_ms, TF_Status* status,
    bool clear_existing_contexts) {
  TFE_ContextSetServerDefWithTimeoutAndRetries(
      ctx, keep_alive_secs, proto, proto_len, init_timeout_in_ms,
      /*retries=*/0, status, clear_existing_contexts);
}

  TF_DeleteTensor(t);
  TF_DeleteStatus(status);
  return th;
}

TFE_TensorHandle* TestScalarTensorHandle(TFE_Context* ctx, bool value) {
  bool data[] = {value};
  TF_Status* status = TF_NewStatus();
  TF_Tensor* t = TFE_AllocateHostTensor(ctx, TF_BOOL, nullptr, 0, status);
  memcpy(TF_TensorData(t), &data[0], TF_TensorByteSize(t));
  TFE_TensorHandle* th = TFE_NewTensorHandleFromTensor(ctx, t, status);

     *
     *    where A.T means `transpose(A)`
     */
    AbstractTensorHandle* upstream_grad = grad_outputs[0];

    // Get transpose attrs
    bool t_a;
    TF_RETURN_IF_ERROR(forward_attrs_.Get("transpose_a", &t_a));

    bool t_b;
    TF_RETURN_IF_ERROR(forward_attrs_.Get("transpose_b", &t_b));

    // Conj each input
    AbstractTensorHandle* conj_output;

      tensorflow::GetBuildXlaOpsPassFlags();
  bool original = flags->tf_xla_enable_lazy_compilation;
  flags->tf_xla_enable_lazy_compilation = enable;
  return original;
}

unsigned char TF_SetTfXlaCpuGlobalJit(unsigned char enable) {
  tensorflow::MarkForCompilationPassFlags* flags =
      tensorflow::GetMarkForCompilationPassFlags();
  bool original = flags->tf_xla_cpu_global_jit;