return ops::BiasAdd(ctx, inputs[0], inputs[1], &outputs[0], "NHWC",
                      "BiasAdd");
}

class CppGradients
    : public ::testing::TestWithParam<std::tuple<const char*, bool, bool>> {
 protected:
  void SetUp() override {
    TF_StatusPtr status(TF_NewStatus());
    TF_SetTracingImplementation(std::get<0>(GetParam()), status.get());
    status_ = StatusFromTF_Status(status.get());

//   outputs = model(inputs)
Status RunModel(Model model, AbstractContext* ctx,
                absl::Span<AbstractTensorHandle* const> inputs,
                absl::Span<AbstractTensorHandle*> outputs, bool use_function);

Status BuildImmediateExecutionContext(bool use_tfrt, AbstractContext** ctx);

// Return a tensor handle with given type, values and dimensions.
template <class T, TF_DataType datatype>

  // Serialize to GraphDef.
  GraphDef graph_def;
  ASSERT_TRUE(GetGraphDef(graph, &graph_def));

  // Validate GraphDef is what we expect.
  bool found_placeholder = false;
  bool found_scalar_const = false;
  bool found_add = false;
  for (const auto& n : graph_def.node()) {
    if (IsPlaceholder(n)) {
      EXPECT_FALSE(found_placeholder);
      found_placeholder = true;

  // `keep_alive_secs` of inactivity.
  virtual Status SetOrUpdateServerDef(const ServerDef& server_def,
                                      bool reset_context, int keep_alive_secs,
                                      int64_t init_timeout_in_ms, int retries,
                                      bool clear_existing_contexts = false) = 0;

  // Initializes context for the local worker and no contexts will be created

  absl::StatusOr<core::RefCountPtr<FunctionRecord>> GetFunctionRecord()
      override {
    return func_record_.GetNewRef();
  }

  // For LLVM style RTTI.
  static bool classof(const AbstractFunction* ptr) {
    return ptr->getKind() == kGraph;
  }

 private:
  core::RefCountPtr<FunctionRecord> func_record_;
};
}  // namespace graph
}  // namespace tracing

    default_version = default_python_version,
    ignore_root_user_error = True,
    python_versions = [
        "3.9",
        "3.10",
        "3.11",
        "3.12",
    ],
    register_coverage_tool = True,
)

                    unsigned char value) {
  desc->node_builder.Attr(attr_name, static_cast<bool>(value));
}

void TF_SetAttrBoolList(TF_OperationDescription* desc, const char* attr_name,
                        const unsigned char* values, int num_values) {
  std::unique_ptr<bool[]> b(new bool[num_values]);
  for (int i = 0; i < num_values; ++i) {
    b[i] = values[i];
  }

    -   Uses `pycpp.sh`, `code_check_changed_files.sh`

These "env" files match up with an environment matrix that roughly covers:

-   Different Python versions
-   Linux, MacOS, and Windows machines (these pool definitions are internal)
-   x86 and arm64
-   CPU-only, or with NVIDIA CUDA support (Linux only), or with TPUs

## How to Test Your Changes to TensorFlow

You may check how your changes will affect TensorFlow by:

  outputs[0] = temp_outputs[1];
  temp_outputs[0]->Unref();
  return absl::OkStatus();
}

class CppGradients
    : public ::testing::TestWithParam<std::tuple<const char*, bool, bool>> {
 protected:
  void SetUp() override {
    TF_StatusPtr status(TF_NewStatus());
    TF_SetTracingImplementation(std::get<0>(GetParam()), status.get());
    status_ = StatusFromTF_Status(status.get());

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(