# Description:
# TensorFlow is a computational framework, primarily for use in machine
# learning applications.

load("@bazel_skylib//lib:selects.bzl", "selects")
load("@bazel_skylib//rules:common_settings.bzl", "bool_flag", "bool_setting")
load("@local_config_cuda//cuda:build_defs.bzl", "if_cuda")
load(
    "//tensorflow:tensorflow.bzl",
    "VERSION",
    "VERSION_MAJOR",
    "check_deps",
    "if_google",
    "if_oss",

      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;

      const std::string& fname, TransactionToken* token,
      std::unique_ptr<ReadOnlyMemoryRegion>* result) override;
  Status FileExists(const std::string& fname, TransactionToken* token) override;
  bool FilesExist(const std::vector<std::string>& files,
                  TransactionToken* token,
                  std::vector<Status>* status) override;
  Status GetChildren(const std::string& dir, TransactionToken* token,

    }
    return tensorflow::errors::Internal("Graph pass runs for more than once!");
  }

 private:
  bool first_call_ = true;
};

// After the graph pass is registered, it takes effect globally and can affect
// other test cases. Define a static variable to switch it on and off.
bool GraphErrorInjectionPass::enabled_ = false;

// Test to ensure that a registered graph optimization pass is only executed

namespace tensorflow {
namespace gradients {
namespace internal {

void CompareNumericalAndAutodiffGradients(
    Model model, Model grad_model, AbstractContext* ctx,
    absl::Span<AbstractTensorHandle* const> inputs, bool use_function,
    double abs_error) {
  auto num_inputs = inputs.size();
  std::vector<AbstractTensorHandle*> outputs(num_inputs);
  auto s = RunModel(grad_model, ctx, inputs, absl::MakeSpan(outputs),

              const std::vector<TF_Operation*>& inputs,
              const std::vector<TF_Operation*>& outputs,
              const std::vector<string>& output_names,
              bool expect_failure = false) {
    DefineT(num_opers, opers, ToOutput(inputs), ToOutput(outputs), output_names,
            expect_failure);
  }

  // Caller must delete[] the returned value

Status RunAndMaybeSum(AbstractContext* ctx, Model forward,
                      absl::Span<AbstractTensorHandle* const> inputs,
                      absl::Span<AbstractTensorHandle*> outputs,
                      bool use_function) {
  AbstractTensorHandle* model_outputs[1];

  // Run the model.
  TF_RETURN_IF_ERROR(
      RunModel(forward, ctx, inputs, model_outputs, use_function));

namespace gradients {
namespace internal {
namespace {
using std::vector;
using tensorflow::TF_StatusPtr;
using tracing::TracingOperation;

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 s = StatusFromTF_Status(status.get());

  if not parsed_args.print and not parsed_args.xml_out_path:
    raise TypeError('`--print` or `--xml-out-path` must be specified')

  return parsed_args


def parse_log(file_path: str,
              verbose: bool = False) -> ResultDictType:
  """Finds ResultStore links, and tries to determine their status."""
  with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
    log_lines = f.read().splitlines()

    *   Added support for `tflite_convert` command line tool in 2.0.
    *   Post-training quantization tool supports quantizing weights shared by
        multiple operations. The models made with versions of this tool will use
        INT8 types for weights and will only be executable interpreters from
        this version onwards.
    *   Post-training quantization tool supports fp16 weights and GPU delegate