// Parses the command line flag strings to the specification of nodes in
// the Graph. `data_types` input string can be empty since the flag is optional.
Status ParseInputArrayInfo(absl::string_view array_names,
                           absl::string_view data_types,
                           absl::string_view shapes,
                           GraphImportConfig::InputArrays* inputs);

    if (output_name.empty()) continue;
    outputs->push_back(output_name);
  }
  return absl::OkStatus();
}

Status ParseInputArrayInfo(absl::string_view array_names,
                           absl::string_view data_types,
                           absl::string_view shapes,
                           GraphImportConfig::InputArrays* inputs) {
  std::vector<string> node_names;
  std::vector<string> node_dtypes;

			field.DataType = Bytes
		}
	}

	if dataTyper, ok := fieldValue.Interface().(GormDataTypeInterface); ok {
		field.DataType = DataType(dataTyper.GormDataType())
	}

	if v, ok := field.TagSettings["AUTOCREATETIME"]; (ok && utils.CheckTruth(v)) || (!ok && field.Name == "CreatedAt" && (field.DataType == Time || field.DataType == Int || field.DataType == Uint)) {
		if field.DataType == Time {
			field.AutoCreateTime = UnixTime

  }

  for (auto& flag : model_flags.input_arrays()) {
    node_names->push_back(flag.name());
    // TOCO doesn't required `data_type` to be filled for every input.
    // If it's not filled, make it an empty string so the importer will use
    // the data type in the NodeDef.
    auto toco_data_type = flag.data_type();
    if (toco_data_type == ::toco::IODataType::IO_DATA_TYPE_UNKNOWN) {
      node_dtypes->push_back("");
    } else {

Klaus Post <******@****.***> 1718033511 -0700

namespace tensorflow {
namespace ops {

namespace {

// Utility function for converting to internal C++ datatypes.
OutputTensor ToOutputTensor(const Output& output) {
  return OutputTensor(output.node(), output.index());
}

// Utility function for converting to internal C++ datatypes.
std::vector<OutputTensor> ToOutputTensors(const std::vector<Output>& outputs) {
  std::vector<OutputTensor> result(outputs.size());

  return tensorflow::internal::PickUnusedPortOrDie();
}

TFE_TensorHandle* TFE_NewTensorHandleFromScalar(TF_DataType data_type,
                                                void* data, size_t len,
                                                TF_Status* status) {
  auto dtype = static_cast<tensorflow::DataType>(data_type);
  DCHECK(tensorflow::DataTypeCanUseMemcpy(dtype));

  tensorflow::Tensor tensor(dtype, tensorflow::TensorShape({}));

                         const std::vector<ControlFlowInfo>& control_flow_info,
                         const TensorShapeProto& shape, Graph* graph_out) {
  DataType data_type = src_node->output_type(src_port);
  TensorProto dummy_proto;
  dummy_proto.set_dtype(data_type);
  *dummy_proto.mutable_tensor_shape() = shape;
  // Don't set any value field in the proto, since it is only going to be used
  // for shape inference.

 */
private
fun sameType(left: DataType, right: DataType) = when (left) {
    is DataClass -> right is DataClass && left.name.qualifiedName == right.name.qualifiedName
    is DataType.BooleanDataType -> right is DataType.BooleanDataType
    is DataType.IntDataType -> right is DataType.IntDataType
    is DataType.LongDataType -> right is DataType.LongDataType
    is DataType.StringDataType -> right is DataType.StringDataType

		{Name: "Name", DBName: "name", BindNames: []string{"Name"}, DataType: schema.String},
		{Name: "Age", DBName: "age", BindNames: []string{"Age"}, DataType: schema.Uint, Size: 64},
		{Name: "Birthday", DBName: "birthday", BindNames: []string{"Birthday"}, DataType: schema.Time},
		{Name: "CompanyID", DBName: "company_id", BindNames: []string{"CompanyID"}, DataType: schema.Int, Size: 64},