# 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 {

  } else {
    EXPECT_TRUE(absl::StrContains(ctx.status().ToString(), expected_output))
        << ctx.status();
  }
}

TEST(ScalarSummaryOpTest, SimpleFloat) {
  int vectorSize = 3;
  Tensor tags(DT_STRING, {vectorSize});
  Tensor values(DT_FLOAT, {vectorSize});
  tags.vec<tstring>()(0) = "tag1";
  tags.vec<tstring>()(1) = "tag2";
  tags.vec<tstring>()(2) = "tag3";
  values.vec<float>()(0) = 1.0f;

["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;

  EXPECT_FALSE(root.status().ok());
}

TEST(CCOpTest, InvalidFinalize) {
  Scope root = Scope::NewRootScope();
  auto read_up_to =
      ops::ReaderReadUpTo(root, Variable(root, {}, DT_STRING),
                          Variable(root, {}, DT_STRING), static_cast<int32>(2));
  EXPECT_FALSE(root.status().ok());
  auto err_msg = std::string(root.status().message());
  EXPECT_NE(err_msg.find("'num_records' passed int32 expected int64"),

  // 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,

      }
      op {
        name: "RestoreV2"
        input_arg {
          name: "prefix"
          type: DT_STRING
        }
        input_arg {
          name: "tensor_names"
          type: DT_STRING
        }
        input_arg {
          name: "shape_and_slices"
          type: DT_STRING
        }
        output_arg {
          name: "tensors"
          type_list_attr: "dtypes"
        }