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;

  EXPECT_TRUE(n->IsConstant());
  Tensor tensor;
  TF_EXPECT_OK(GetNodeAttr(n->attrs(), "value", &tensor));
  DataType dtype;
  TF_EXPECT_OK(GetNodeAttr(n->attrs(), "dtype", &dtype));
  EXPECT_EQ(tensor.dtype(), dtype);
  test::ExpectTensorEqual<T>(tensor, test::AsTensor(values, shape));
}

void ExpectTypeAndShape(const Node* n, DataType expected_dtype,
                        TensorShape expected_shape) {
  EXPECT_TRUE(n->IsConstant());

// device memory. It also owns the PjRtBuffer.
//
// TODO(b/289001822): Create a unit test to cover this function.
absl::StatusOr<Tensor> MakeTensorFromPjRtBuffer(
    DataType dtype, const TensorShape& shape,
    std::unique_ptr<xla::PjRtBuffer> pjrt_buffer);

// For TensorFlow internal use only.
class PjRtTensorBufferUtil {
 public:

 * 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

//
Status VarHandleOp(AbstractContext* ctx, AbstractTensorHandle** resource, DataType dtype, const PartialTensorShape shape, const char* container = "", const char* shared_name = "", const char* debug_name = "", absl::Span<string const> allowed_devices = {}, const char* name = nullptr, const char* raw_device_name = nullptr);

            val fqn = when (val ref = function.dataClass) {
                is DataTypeRef.Name -> ref.fqName.toString()
                is DataTypeRef.Type -> (ref.dataType as? DataClass)?.name?.qualifiedName
                    ?: ref.dataType.toString()
            }
            append(fqn)
            append(".")
        }
        append(function.simpleName)
        append("#")
        append(invocationId)

// Return a tensor handle with given type, values and dimensions.
template <class T, TF_DataType datatype>
TFE_TensorHandle* TestTensorHandleWithDims(TFE_Context* ctx, const T* data,
                                           const int64_t* dims, int num_dims) {
  TF_Status* status = TF_NewStatus();
  TF_Tensor* t = TFE_AllocateHostTensor(ctx, datatype, dims, num_dims, status);
  memcpy(TF_TensorData(t), data, TF_TensorByteSize(t));

				relation.FieldSchema, schema, field.Name)
			return
		}

		// use same data type for foreign keys
		if copyableDataType(primaryKeyField.DataType) {
			relation.Polymorphic.PolymorphicID.DataType = primaryKeyField.DataType
		}
		relation.Polymorphic.PolymorphicID.GORMDataType = primaryKeyField.GORMDataType
		if relation.Polymorphic.PolymorphicID.Size == 0 {