Tensor* cpu_tensor, StatusCallback done) {
  DataType dtype = EncodePrimitiveTypeAsDataType(shape_.element_type()).value();
  TensorShape tensor_shape;
  Status status = XLAShapeToTensorShape(shape_, &tensor_shape);
  if (!status.ok()) {
    done(status);
    return;
  }

  *cpu_tensor = Tensor(dtype, tensor_shape);

  status = stream_->Memcpy(cpu_tensor->data(), device_memory_base_,

				case abi.Interface:
					ityp := (*interfacetype)(unsafe.Pointer(f.fint))
					// set up with empty interface
					(*eface)(r)._type = &f.ot.Type
					(*eface)(r).data = f.arg
					if len(ityp.Methods) != 0 {
						// convert to interface with methods
						// this conversion is guaranteed to succeed - we checked in SetFinalizer
						(*iface)(r).tab = assertE2I(ityp, (*eface)(r)._type)
					}
				default:

    @Test
    fun `handles the hidden properties correctly`() {
        val aType = schema.dataClassesByFqName.getValue(DefaultFqName.parse(HasHiddenProperty::class.qualifiedName!!))
        assertTrue { aType.properties.single { it.name == "y" }.isHiddenInDsl }

        val result = schema.resolve(
            """
            x = 1
            y = 2

  class AddOneModel(tf.keras.Model):

    def call(self, x):
      return x + 1

  model = AddOneModel()

  x_train = tf.constant([1, 2, 3, 4, 5], dtype=tf.float32)
  y_train = tf.constant([2, 3, 4, 5, 6], dtype=tf.float32)

  model.compile(optimizer='sgd', loss='mse')
  model.fit(x_train, y_train, epochs=1)

  path = tempdir + '/add_one_model'
  model.save(path)
  return path

// guessIsMetricsReq - returns true if incoming request looks
// like metrics request
func guessIsMetricsReq(req *http.Request) bool {
	if req == nil {
		return false
	}
	aType := getRequestAuthType(req)
	return (aType == authTypeAnonymous || aType == authTypeJWT) &&
		req.URL.Path == minioReservedBucketPath+prometheusMetricsPathLegacy ||
		req.URL.Path == minioReservedBucketPath+prometheusMetricsV2ClusterPath ||

class ConvertTensorTest : public ::testing::Test {
 protected:
  template <typename T>
  void VerifyConversion(std::initializer_list<T> values, DataType dtype,
                        mlir::Type expected_ty) {
    mlir::Builder b(expected_ty.getContext());
    Tensor tensor(dtype, TensorShape({static_cast<int64_t>(values.size())}));
    tensor.flat<T>().setValues(values);

    auto value_or = ConvertTensor(tensor, &b);

  for (auto operand_type_and_idx : llvm::enumerate(op.getOperandTypes())) {
    Type operand_type = operand_type_and_idx.value();
    int index = operand_type_and_idx.index();
    tensorflow::tpu::TPUCompileMetadataProto::Arg* arg = metadata->add_args();
    tensorflow::DataType dtype;
    tensorflow::Status status =
        tensorflow::ConvertToDataType(operand_type, &dtype);
    if (!status.ok())
      return op.emitOpError(

  %0 = "tf.Const"() {device = "/device:CPU:0", value = dense<9> : tensor<i32>} : () -> tensor<i32>
  %1 = "tf.ReadVariableOp"(%arg) {device = "/device:CPU:0", dtype = i32} : (tensor<!tf_type.resource<tensor<i32>>>) -> tensor<i32>
  %2 = "tf.Less"(%1, %0) {device = "/device:CPU:0"} : (tensor<i32>, tensor<i32>) -> tensor<i1>
  func.return %2 : tensor<i1>
}

}

Status ConvertAttribute(mlir::Type type, bool remove_ref_type,
                        AttrValue* value) {
  DataType dtype;
  TF_RETURN_IF_ERROR(ConvertToDataType(type, &dtype));
  if (tensorflow::IsRefType(dtype)) dtype = tensorflow::RemoveRefType(dtype);
  value->set_type(dtype);
  return absl::OkStatus();
}

Status ConvertAttribute(const mlir::TypeAttr& type, bool remove_ref_type,

    }

    se::Stream* const src_compute_stream = src_xla_context->stream();
    TF_RET_CHECK(src_compute_stream != nullptr);
    TF_RET_CHECK(input->dtype() == output->dtype())
        << "input type: " << DataTypeString(input->dtype()) << " output type "
        << DataTypeString(output->dtype());
    TF_RET_CHECK(input->shape() == output->shape());
    TF_RET_CHECK(DMAHelper::CanUseDMA(input));
    auto* const src_compute_stream_impl =