handle_ = other.handle_;
  handle_->Ref();
}
TapeTensor::~TapeTensor() { handle_->Unref(); }

int64_t TapeTensor::GetID() const { return ToId(handle_); }

tensorflow::DataType TapeTensor::GetDType() const {
  return handle_->DataType();
}
AbstractTensorHandle* TapeTensor::GetHandle() const { return handle_; }

AbstractTensorHandle* TapeTensor::ZerosLike() const { return nullptr; }

class TapeVSpace

  TFE_TensorHandle* input_eager =
      TestTensorHandleWithDims<T, datatype>(eager_ctx, data, dims, num_dims);
  *tensor =
      unwrap(TF_CreateAbstractTensorFromEagerTensor(input_eager, status.get()));
  return absl::OkStatus();
}

// Return a scalar tensor handle with given value.
template <class T, TF_DataType datatype>
Status TestScalarTensorHandle(AbstractContext* ctx, const T value,

    return mlir::cast<mlir::StringAttr>(index_path[0]).getValue();
  }
  return "";
}

absl::StatusOr<std::pair<tensorflow::DataType, tensorflow::PartialTensorShape>>
ProcessTensorSpec(mlir::TensorType type) {
  tensorflow::DataType dtype;
  TF_RETURN_IF_ERROR(
      ConvertScalarTypeToDataType(type.getElementType(), &dtype));

  if (!type.hasRank())

#include "tensorflow/core/platform/types.h"

namespace tensorflow {
namespace internal {

Status CreateUninitializedResourceVariable(ImmediateExecutionContext* ctx,
                                           DataType dtype, TensorShape shape,
                                           const char* raw_device_name,
                                           ImmediateTensorHandlePtr* handle) {
  ImmediateOpPtr varhandle_op(ctx->CreateOperation());

@ToolingModelContract(subTypes = [
    DataTypeRef.Type::class,
    DataTypeRef.Name::class
])
sealed interface DataTypeRef : Serializable {
    interface Type : DataTypeRef {
        val dataType: DataType
    }

    interface Name : DataTypeRef {
        val fqName: FqName
    }

        val topLevelReceiverName = topLevelReceiver.fqName

        return DefaultAnalysisSchema(
            dataTypes.single { it.name == topLevelReceiverName },
            dataTypes.associateBy { it.name },
            extFunctions,
            extObjects,
            defaultImports.toSet()
        )
    }

    private
    val KClass<*>.fqName

 public:
  explicit GraphTensor(TF_Output output, TF_Graph* graph)
      : TracingTensorHandle(kGraph), output_(output), graph_(graph) {}

  tensorflow::DataType DataType() const override {
    return static_cast<tensorflow::DataType>(TF_OperationOutputType(output_));
  }

  tensorflow::Status Shape(
      tensorflow::PartialTensorShape* shape) const override {
    DCHECK(shape != nullptr);
    TF_Status status;

// Convert the scalar type of a TFLite tensor to the corresponding
// Tensorflow type
tensorflow::DataType TflTypeToTfType(tflite::TensorType type);

// Convert the Tensorflow scalar type to the corresponding TFLite type
absl::StatusOr<tflite::TensorType> TfTypeToTflType(tensorflow::DataType type);

// Returns element type from attribute Type 'type_attr'.
mlir::Type GetShapeStrippedType(mlir::TypeAttr type_attr);

#include "llvm/Support/raw_ostream.h"
#include "tensorflow/core/framework/types.pb.h"

// Returns whether the given dtype is a quantization type in TensorFlow.
static bool IsQuantizationType(tensorflow::DataType dtype) {
  switch (dtype) {
    case tensorflow::DT_QINT8:
    case tensorflow::DT_QUINT8:
    case tensorflow::DT_QINT16:
    case tensorflow::DT_QUINT16:
    case tensorflow::DT_QINT32:
      return true;

 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package org.gradle.internal.declarativedsl.analysis

import org.gradle.declarative.dsl.schema.DataType
import org.gradle.declarative.dsl.schema.FunctionSemantics
import org.gradle.internal.declarativedsl.language.Assignment
import org.gradle.internal.declarativedsl.language.Expr