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

  std::vector<int64_t> dimvec(num_dims);
  for (int i = 0; i < num_dims; ++i) {
    dimvec[i] = static_cast<int64_t>(dims[i]);
  }

  Tensor ret(static_cast<tensorflow::DataType>(dtype),
             tensorflow::TensorShape(dimvec), buf);
  buf->Unref();
  size_t elem_size = TF_DataTypeSize(dtype);
  if (elem_size > 0 && len < (elem_size * ret.NumElements())) {
    return nullptr;
  }

Status DemangleTensor(absl::string_view str, TensorProto* proto) {
  return ParseTextProto(str, kTensorPrefix, proto);
}

string MangleDataType(const DataType& dtype) {
  return absl::StrCat(kDataTypePrefix, DataType_Name(dtype));
}

Status DemangleDataType(absl::string_view str, DataType* proto) {
  absl::string_view pbtxt;
  TF_RETURN_IF_ERROR(ConsumePrefix(str, kDataTypePrefix, &pbtxt));
  if (!DataType_Parse(string(pbtxt), proto)) {

Type TensorFlowExecutorDialect::parseType(DialectAsmParser &parser) const {
  StringRef data_type;
  if (parser.parseKeyword(&data_type)) return Type();

  if (data_type == "control") return ControlType::get(getContext());
  if (data_type == "token") return TokenType::get(getContext());
  parser.emitError(parser.getNameLoc())
      << "unknown tf_executor type: " << data_type;
  return nullptr;
}

import org.gradle.declarative.dsl.schema.DataClass
import org.gradle.declarative.dsl.schema.DataParameter
import org.gradle.declarative.dsl.schema.DataProperty
import org.gradle.declarative.dsl.schema.DataType
import org.gradle.declarative.dsl.schema.DataTypeRef
import org.gradle.declarative.dsl.schema.FqName
import org.gradle.declarative.dsl.schema.FunctionSemantics
import org.gradle.declarative.dsl.schema.ParameterSemantics

package org.gradle.internal.declarativedsl.analysis

import org.gradle.declarative.dsl.schema.DataClass
import org.gradle.declarative.dsl.schema.DataProperty
import org.gradle.declarative.dsl.schema.DataType
import org.gradle.declarative.dsl.schema.FqName
import org.gradle.internal.declarativedsl.language.AccessChain
import org.gradle.internal.declarativedsl.language.LanguageTreeElement
import org.gradle.internal.declarativedsl.language.LocalValue