)

updater_config_repository(name = "updater_config_repository")

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

    TF_SetStatus(&s, static_cast<TF_Code>(result.error_code()),
                 std::string(result.error_message()).data());
    if (TF_GetCode(&s) != TF_Code::TF_OK) {
      tensorflow::CoordinationServiceError error;
      *error.mutable_source_task() = result.error_payload().source_task();
      TF_SetPayload(&s, tensorflow::CoordinationErrorPayloadKey().data(),
                    error.SerializeAsString().c_str());
    }

// Set the config in TF_ContextOptions.options.
// config should be a serialized tensorflow.ConfigProto proto.
// If config was not parsed successfully as a ConfigProto, record the
// error information in *status.
TF_CAPI_EXPORT extern void TFE_ContextOptionsSetConfig(
    TFE_ContextOptions* options, const void* proto, size_t proto_len,
    TF_Status* status);

# script to run pip-compile for keras, tensorboard, estimator deps.
# if there is a change in requirements.in then all lock files will be updated
# accordingly.

# -e: abort script if one command fails
# -u: error if undefined variable used
# -o pipefail: entire command fails if pipe fails. watch out for yes | ...
# -o history: record shell history
set -euo pipefail -o history

# Check for required arguments
if [ -z "$1" ]; then

    ASSERT_EQ(errors::OK, s.code()) << s.message();
    s = dyn_cast<tracing::TracingContext>(ctx.get())->AddParameter(
        DT_FLOAT, shape, &x_raw);
    ASSERT_EQ(errors::OK, s.code()) << s.message();
    x.reset(x_raw);
  }

  PartialTensorShape shape;
  Status s = x->Shape(&shape);
  ASSERT_EQ(errors::OK, s.code()) << s.message();
  ASSERT_FALSE(shape.unknown_rank());

    float* expected_grad, int num_grad, bool use_function,
    double abs_error = 1e-2) {
  Status s;
  AbstractTensorHandlePtr numerical_grad;
  {
    AbstractTensorHandle* numerical_grad_raw;
    s = CalcNumericalGrad(ctx, model, inputs, input_index, use_function,
                          &numerical_grad_raw);
    ASSERT_EQ(errors::OK, s.code()) << s.message();
    numerical_grad.reset(numerical_grad_raw);
  }

# Note: this is excluded on the full code base, since any submitted code must
# have passed Google's internal style guidelines.
@test "Check pylint for Python files" {
    echo "Python formatting is recommended. Here are the pylint errors:"
    echo "============================="
    grep -e "\.py$" $BATS_FILE_TMPDIR/changed_files > $BATS_TEST_TMPDIR/files || true
    if [[ ! -s $BATS_TEST_TMPDIR/files ]]; then return 0; fi

  virtual Status InputLength(const char* input_name, int* length) = 0;
  virtual Status OutputLength(const char* output_name, int* length) = 0;

  // Set stack trace to be used for potential async error reporting.
  virtual void SetStackTrace(ManagedStackTrace stack_trace) = 0;

  virtual const tensorflow::AbstractOpAttrs* GetOpAttrs() const = 0;
  virtual void AddAttrs(const AbstractOpAttrs* op_attrs) = 0;

build --copt="-mtune=generic" --copt="-march=armv8-a" --copt="-O3"

# Test-related settings below this point.
test --build_tests_only --keep_going --test_output=errors --verbose_failures=true
test --test_timeout=500,900,-1,-1
# Give only the list of failed tests at the end of the log
test --test_summary=short

# "nonpip" tests are regular py_test tests.

      "' (available: ");
  // Ensure deterministic (sorted) order in the error message
  std::set<string> factories_sorted;
  for (const auto& factory : GetFactories())
    factories_sorted.insert(factory.first);
  const char* comma = "";
  for (const string& factory : factories_sorted) {
    msg += comma + factory;
    comma = ", ";
  }
  msg += ")";

  return errors::InvalidArgument(msg.c_str());
}