TensorSpec::TensorSpec(PartialTensorShape shape, DataType dtype)
    : shape_(std::move(shape)), dtype_(dtype) {}

TensorSpec::TensorSpec(const TensorSpecProto& proto)
    : shape_(proto.shape()), dtype_(proto.dtype()) {}

const PartialTensorShape& TensorSpec::shape() const { return shape_; }

DataType TensorSpec::dtype() const { return dtype_; }

                                                       TF_Status* status) const;

  // Construct a parallel tensor consisting of the scalar values from `values`.
  template <typename DataType>
  std::unique_ptr<ParallelTensor> ScalarsFromSequence(
      absl::Span<const DataType> values, TFE_Context* context,
      TF_Status* status) const;

  // A parallel tensor with scalar integers numbering component devices.

    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())

  virtual bool GetBool(absl::string_view attr_name, bool* result) const = 0;
  virtual bool GetType(absl::string_view attr_name, DataType* result) const = 0;
  virtual Status GetTypeList(
      absl::string_view attr_name,
      absl::InlinedVector<DataType, 4>* type_list) const = 0;

 private:
  const AbstractOpAttrsKind kind_;
};

}  // namespace tensorflow

#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

func (c *StandardClaims) UnmarshalJSON(b []byte) (err error) {
	return jsonparser.ObjectEach(b, func(key []byte, value []byte, dataType jsonparser.ValueType, _ int) error {
		if len(key) == 0 {
			return nil
		}
		switch key[0] {
		case 'a':
			if string(key) == "accessKey" {
				if dataType != jsonparser.String {
					return errors.New("accessKey: Expected string")
				}

#include "tensorflow/core/lib/llvm_rtti/llvm_rtti.h"
#include "tensorflow/core/platform/errors.h"
#include "tensorflow/core/platform/logging.h"

namespace tensorflow {

Variable::Variable(ImmediateExecutionContext* ctx, DataType dtype,
                   TensorShape shape, absl::optional<std::string> name,
                   ImmediateTensorHandlePtr handle)
    : TensorHandleConvertible(std::move(handle)),

namespace tensorflow {
namespace ops {

// Creates a handle to a Variable resource.
Status VarHandleOp(AbstractContext* ctx, AbstractTensorHandle** resource,
                   DataType dtype, const PartialTensorShape shape,
                   const char* container = "", const char* shared_name = "",
                   absl::Span<string const> allowed_devices = {},
                   const char* name = nullptr,