switch (default_value.value_case()) {
    case tensorflow::AttrValue::kS: {
      const string& v = default_value.s();
      TFE_OpSetAttrString(op, attr_name, v.data(), v.size());
      break;
    }
    case tensorflow::AttrValue::kI:
      TFE_OpSetAttrInt(op, attr_name, static_cast<int64_t>(default_value.i()));
      break;
    case tensorflow::AttrValue::kF:

  delete counter;
}

TFE_MonitoringCounterCell* TFE_MonitoringGetCellCounter0(
    TFE_MonitoringCounter0* counter) {
  return static_cast<TFE_MonitoringCounterCell*>(
      static_cast<void*>(counter->counter->GetCell()));
}

TFE_MonitoringCounter1* TFE_MonitoringNewCounter1(const char* name,
                                                  TF_Status* status,

      return errors::Internal("Expected 1 output but found ", num_outputs);
    }
    auto* t = dyn_cast<GraphTensor>(outputs[0]);
    if (!t) {
      return tensorflow::errors::InvalidArgument(
          "Unable to cast input to GraphTensor");
    }
    inputs_.push_back(t->output_);
    *output = tensorflow::down_cast<TracingTensorHandle*>(outputs[0]);
    return absl::OkStatus();
  }

namespace tracing {

Status MaybeSetOpName(AbstractOperation* op, const char* op_name) {
  if (isa<TracingOperation>(op)) {
    TF_RETURN_IF_ERROR(dyn_cast<TracingOperation>(op)->SetOpName(op_name));
  }
  if (isa<gradients::TapeOperation>(op)) {
    TF_RETURN_IF_ERROR(MaybeSetOpName(
        dyn_cast<gradients::TapeOperation>(op)->GetBackingOperation(),
        op_name));
  }
  return absl::OkStatus();
}
}  // namespace tracing

  }
  AbstractContextPtr ctx(BuildFunction("test_fn"));
  AbstractTensorHandlePtr x;
  {
    tracing::TracingTensorHandle* x_raw = nullptr;
    PartialTensorShape shape;
    Status 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;

  tracing::TracingTensorHandle* handle = nullptr;
  for (auto input : inputs) {
    PartialTensorShape shape;
    TF_RETURN_IF_ERROR(input->Shape(&shape));
    TF_RETURN_IF_ERROR(dyn_cast<tracing::TracingContext>(ctx)->AddParameter(
        input->DataType(), shape, &handle));
    params->emplace_back(handle);
  }
  return absl::OkStatus();
}

// Runs `model` maybe wrapped in a function.

                                         const tensorflow::tstring& value) {
  TF_Status* status = TF_NewStatus();
  TF_Tensor* t = TFE_AllocateHostTensor(ctx, TF_STRING, nullptr, 0, status);
  tstring* data = static_cast<tstring*>(TF_TensorData(t));
  *data = value;
  TFE_TensorHandle* th = TFE_NewTensorHandleFromTensor(ctx, t, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TF_DeleteTensor(t);

        *   Use the free functions such as
            `tensorflow::errors::IsInvalidArgument` if needed.
        *   In the last resort, use
            e.g.`static_cast<tensorflow::errors::Code>(error::Code::INVALID_ARGUMENT)`
            or `static_cast<int>(code)` for comparisons.
*   `tensorflow::StatusOr` will also become in the future an alias to
    `absl::StatusOr`, so use `StatusOr::value` instead of

inline bool IsDtypeTrainable(DataType dtype) {
  switch (dtype) {
    case DT_HALF:
    case DT_BFLOAT16:
    case DT_FLOAT:
    case DT_DOUBLE:
    case DT_COMPLEX64:
    case DT_COMPLEX128:
    case DT_RESOURCE:
    case DT_VARIANT:
      return true;
    case DT_QINT8:
    case DT_QINT16:
    case DT_QINT32:
    case DT_QUINT8:
    case DT_QUINT16:
      return tensorflow::flags::Global()

TFCI_MACOS_BAZEL_TEST_DIR_PATH="/Volumes/BuildData/bazel_output"
TFCI_OUTPUT_DIR=build_output
TFCI_WHL_BAZEL_TEST_ENABLE=1
TFCI_WHL_SIZE_LIMIT=240M
TFCI_WHL_SIZE_LIMIT_ENABLE=1

# 3.11 is the system python on our images
case $TFCI_PYTHON_VERSION in
3.11)
  TFCI_MACOS_PYENV_INSTALL_ENABLE=0
  ;;
*)
  TFCI_MACOS_PYENV_INSTALL_ENABLE=1
  ;;