TF_DataType value) {
  auto iter = builder->attr_names.insert(attr_name).first;
  builder->Set(*iter, static_cast<tensorflow::DataType>(value));
}

void TF_AttrBuilderSetTypeList(TF_AttrBuilder* builder, const char* attr_name,
                               const TF_DataType* values, int num_values) {
  auto iter = builder->attr_names.insert(attr_name).first;

						columnType)
				}
			case "name":
				dataType := DB.Dialector.DataTypeOf(stmt.Schema.LookUpField(columnType.Name()))
				if !strings.Contains(strings.ToUpper(dataType), strings.ToUpper(columnType.DatabaseTypeName())) {
					t.Fatalf("column name type should be correct, name: %v, length: %v, expects: %v, column: %#v",
						columnType.Name(), columnType.DatabaseTypeName(), dataType, columnType)
				}

 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;

  }

  // Returns whether any tensor in a list of tensors is being watched and has
  // a trainable dtype.
  bool ShouldRecord(absl::Span<const int64_t> tensor_ids,
                    absl::Span<const tensorflow::DataType> dtypes) const;

  // Adds this tensor to the list of watched tensors.
  //
  // This is a no-op if the tensor is already being watched either from an

  EXPECT_EQ(TF_FLOAT, static_cast<TF_DataType>(tensorflow::DT_FLOAT));
  EXPECT_EQ(TF_DOUBLE, static_cast<TF_DataType>(tensorflow::DT_DOUBLE));
  EXPECT_EQ(TF_INT32, static_cast<TF_DataType>(tensorflow::DT_INT32));
  EXPECT_EQ(TF_UINT8, static_cast<TF_DataType>(tensorflow::DT_UINT8));
  EXPECT_EQ(TF_INT16, static_cast<TF_DataType>(tensorflow::DT_INT16));
  EXPECT_EQ(TF_INT8, static_cast<TF_DataType>(tensorflow::DT_INT8));

      "TFE_DeleteTensorHandle", tensorflow::profiler::TraceMeLevel::kInfo);
  if (h) {
    tensorflow::unwrap(h)->Unref();
  }
}

TF_DataType TFE_TensorHandleDataType(TFE_TensorHandle* h) {
  return static_cast<TF_DataType>(tensorflow::unwrap(h)->DataType());
}

int TFE_TensorHandleNumDims(TFE_TensorHandle* h, TF_Status* status) {
  if (h == nullptr) {

#include "tensorflow/c/kernels.h"

// A create function. This will never actually get called in this test, it's
// just nice to know that it compiles.
void* create(TF_OpKernelConstruction* ctx) {
  TF_DataType type;
  TF_Status* s = TF_NewStatus();
  TF_OpKernelConstruction_GetAttrType(ctx, "foobar", &type, s);
  TF_DeleteStatus(s);
  return NULL;
}

                    TF_DataType value) {
  desc->node_builder.Attr(attr_name, static_cast<DataType>(value));
}

void TF_SetAttrTypeList(TF_OperationDescription* desc, const char* attr_name,
                        const TF_DataType* values, int num_values) {
  desc->node_builder.Attr(
      attr_name, ArraySlice<const DataType>(
                     reinterpret_cast<const DataType*>(values), num_values));
}

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

			parsedField, ok = s.FieldsByName[f.Name]
		}

		if !ok {
			t.Errorf("schema %v failed to look up field with name %v", s, f.Name)
		} else {
			tests.AssertObjEqual(t, parsedField, f, "Name", "DBName", "BindNames", "DataType", "PrimaryKey", "AutoIncrement", "Creatable", "Updatable", "Readable", "HasDefaultValue", "DefaultValue", "NotNull", "Unique", "Comment", "Size", "Precision", "TagSettings")

			if f.DBName != "" {