# CHECK: tf_executor.fetch %[[output]] : tensor<*xi64>

node {
  name: "serilaized"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_STRING
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_STRING
        tensor_shape {
          dim {
            size: 1
          }
        }
        string_val: ""
      }
    }
  }
}
node {

    key: "dtype"
    value {
      type: DT_STRING
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_STRING
        tensor_shape {
          dim {
          }
        }
      }
    }
  }
}
node {
  name: "ParseExample/ParseExampleV2/sparse_keys"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_STRING
    }
  }
  attr {

["tfdtype$DT_FLOAT", "tfdtype$DT_FLOAT"], dense_shapes = [#tf_type.shape<>, #tf_type.shape<>], device = "", num_sparse = 2 : i64, ragged_split_types = [], ragged_value_types = [], resultSegmentSizes = array<i32: 2, 2, 2, 2, 0, 0>, sparse_types = ["tfdtype$DT_STRING", "tfdtype$DT_INT64"]} : (tensor<32x!tf_type.string>, tensor<0x!tf_type.string>, tensor<2x!tf_type.string>, tensor<2x!tf_type.string>, tensor<0x!tf_type.string>, tensor<0xf32>, tensor<0xf32>) -> (tensor<?x2xi64>, tensor<?x2xi64>, tensor<?x!tf_type.string>,...

    case tflite::TensorType_UINT32:
      return tensorflow::DT_UINT32;
    case tflite::TensorType_INT64:
      return tensorflow::DT_INT64;
    case tflite::TensorType_STRING:
      return tensorflow::DT_STRING;
    case tflite::TensorType_UINT8:
      return tensorflow::DT_UINT8;
    case tflite::TensorType_UINT16:
      return tensorflow::DT_UINT16;
    case tflite::TensorType_UINT64:
      return tensorflow::DT_UINT64;

  // Add a _Retval node to loop body.
  NodeDefBuilder ret_builder(
      absl::StrCat("recv_oc_while_body_ret_", while_node_name), "_Retval");
  ret_builder.Attr("T", DT_STRING);
  ret_builder.Attr("index", 0);
  ret_builder.Input(key_arg->name(), 0, DT_STRING);
  NodeDef ret_def;
  TF_RETURN_IF_ERROR(ret_builder.Finalize(&ret_def));
  TF_ASSIGN_OR_RETURN(Node * ret_node, body_graph->AddNode(ret_def));

    case DT_BFLOAT16:
    case DT_HALF:
    case DT_FLOAT8_E5M2:
    case DT_FLOAT8_E4M3FN:
      return ConvertTensorOfCustomFloatType(input_tensor, type);
    case DT_STRING:
      return ConvertStringTensor(input_tensor, type);
    default:
      // TODO(hinsu): Remove mangling now that there is a special attribute.
      return ElementsAttr(

      TF_RETURN_IF_ERROR(ValidateFunctionNotRecursive(function));
    }
  }

  return absl::OkStatus();
}

Tensor CreateStringTensor(const string& value) {
  Tensor tensor(DT_STRING, TensorShape({}));
  tensor.scalar<tstring>()() = value;
  return tensor;
}

void AddAssetsTensorsToInputs(const StringPiece export_dir,

  TF_RETURN_WITH_CONTEXT_IF_ERROR(
      bundle_reader->Lookup(kObjectGraphProtoKey, &object_graph_tensor),
      "SavedModel checkpoint does not contain object graph.");
  if (object_graph_tensor.dtype() != DT_STRING ||
      object_graph_tensor.dims() != 0 ||
      object_graph_tensor.NumElements() != 1) {
    return absl::Status(
        absl::StatusCode::kFailedPrecondition,

  tsl::Set_TF_Status_from_Status(status, cc_status);
}

#endif  // LIBTPU_EXCLUDE_C_API_IMPL

namespace tensorflow {

void TensorInterface::Release() {
  if (Type() == DT_STRING && NumElements() > 0) {
    TF_TString* data = static_cast<TF_TString*>(Data());
    if (CanMove() && data != nullptr) {
      for (int64_t i = 0; i < NumElements(); ++i) {
        TF_TString_Dealloc(&data[i]);
      }

}

TEST(ConvertTypeToTensorTypeTest, ConvertStringTensor) {
  mlir::MLIRContext context;
  RegisterDialects(context);
  mlir::Builder b(&context);

  // Create the sample tensor to convert.
  Tensor tensor(DT_STRING, TensorShape({1, 2, 2, 1}));
  EXPECT_EQ(4, tensor.NumElements());
  auto Tt = tensor.flat<tstring>();
  Tt.setValues({"one", "two", "three", "four"});
  auto value_or_status = ConvertTensor(tensor, &b);