//
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 {

    data class DefaultType(override val dataType: DataType) : DataTypeRef.Type {
        override fun toString(): String = dataType.toString()
    }

    @Serializable
    @SerialName("dataTypeRefName")
    data class DefaultName(override val fqName: FqName) : DataTypeRef.Name {
        override fun toString(): String = fqName.simpleName
    }
}


val DataType.ref: DataTypeRef

  llvm::ArrayRef<llvm::StringRef> input_names;
  llvm::ArrayRef<
      std::pair<tensorflow::DataType, tensorflow::PartialTensorShape>>
      input_specs;
  llvm::ArrayRef<llvm::StringRef> input_devices;

  // The following are metadata for outputs.
  llvm::ArrayRef<llvm::StringRef> output_names;
  llvm::ArrayRef<
      std::pair<tensorflow::DataType, tensorflow::PartialTensorShape>>
      output_specs;

}
Status TapeOperation::SetAttrTypeList(const char* attr_name,
                                      const DataType* values, int num_values) {
  forward_op_.attrs.Set(attr_name,
                        gtl::ArraySlice<const DataType>(values, num_values));
  return parent_op_->SetAttrTypeList(attr_name, values, num_values);
}
Status TapeOperation::SetAttrBoolList(const char* attr_name,

 public:
  explicit MlirTensor(Value value)
      : TracingTensorHandle(kMlir), value_(value) {}

  tensorflow::DataType DataType() const override {
    tensorflow::DataType type;
    Status s = ConvertToDataType(value_.getType(), &type);
    if (!s.ok()) {
      return tensorflow::DT_INVALID;
    }
    return type;
  }

  tensorflow::Status Shape(

  static TensorBuffer* Buffer(const Tensor& tensor) { return tensor.buf_; }
  static Tensor MakeTensor(TF_DataType type, const TensorShape& shape,
                           TensorBuffer* buf) {
    return Tensor(static_cast<DataType>(type), shape, buf);
  }
};

// Allocates tensor data buffer using specified allocator.
// `operation` is a name for this operation.
void* allocate_tensor(const char* operation, size_t len, Allocator* allocator);

func (m Migrator) DataTypeOf(field *schema.Field) string {
	fieldValue := reflect.New(field.IndirectFieldType)
	if dataTyper, ok := fieldValue.Interface().(GormDataTypeInterface); ok {
		if dataType := dataTyper.GormDBDataType(m.DB, field); dataType != "" {
			return dataType
		}
	}

	return m.Dialector.DataTypeOf(field)
}

// FullDataTypeOf returns field's db full data type