}
  const TensorProto& tensor_proto = attr_name_and_value->second.tensor();
  return internal::TensorProtoToConstant(ctx, tensor_proto, output);
}

// Perform some basic sanity checks on SavedConcreteFunction's input and
// output signatures with respect to the corresponding FunctionDef's input
// and output args.
Status ValidateSavedFunctionCompatibleWithFunctionDef(
    const SavedConcreteFunction& saved_concrete_function,

    graph_def: graph_pb2.GraphDef,
) -> graph_pb2.GraphDef:
  """Restores the output tensor names of the converted model.

  During the conversion, the output tensor names of the original model are
  embedded in the `tf_saved_model.index_path` attribute of the RetVal nodes and
  might become the name of Retval nodes as well (with an index suffix if there
  are multiple output tensors from one node). Since Retval nodes are not used in

     * offers a convenient way of connecting together task inputs and outputs. (For a deeper understanding of
     * the topic see the <a href="https://docs.gradle.org/current/userguide/lazy_configuration.html">Lazy Configuration</a>
     * section of the Gradle manual.)</p>
     *
     * <p>
     * Task inputs and outputs often take the form of {@link Provider providers} or {@link Property properties},

    std::vector<MaybeParallelTensorOwned> outputs;
    outputs.reserve(t->num_tensors());
    for (int i = 0; i < t->num_tensors(); ++i) {
      TensorHandlePtr this_output(
          TFE_TensorHandleCopySharingTensor(t->tensor(i), status));
      outputs.emplace_back(std::move(this_output));
      if (TF_GetCode(status) != TF_OK) return result;
    }
    result.emplace(std::move(outputs));
    return result;
  }

                    final List<String> outputStrings = split(sides.get(1), ",");
                    outputs = new String[outputStrings.size()];
                    for (int i = 0; i < outputs.length; i++) {
                        outputs[i] = unescape(outputStrings.get(i)).trim();
                    }

                    if (inputs.length > 0 && outputs.length > 0) {
                        id++;

`UP-TO-DATE`:: *Task's outputs did not change.*
* Task has outputs and inputs but they have not changed. See <<incremental_build.adoc#incremental_build,Incremental Build>>.
* Task has actions, but the task tells Gradle it did not change its outputs.

  %0:2 = tf_executor.graph {
    %outputs:2, %control = tf_executor.island {
      %1 = "tf.Const"() {value = dense<0.000000e+00> : tensor<2048xf32>} : () -> tensor<2048xf32>
      tf_executor.yield %1, %arg0 : tensor<2048xf32>, tensor<?x?x?x?xbf16>
    }
    tf_executor.fetch %outputs#0, %outputs#1 : tensor<2048xf32>, tensor<?x?x?x?xbf16>
  }

            contextualLabel == 'Type \'MyTask\' property \'doubleIterableOptions.*.*.notAnnotated\' is missing an input or output annotation'
            details == 'A property without annotation isn\'t considered during up-to-date checking'
            solutions == [
                'Add an input or output annotation',
                'Mark it as @Internal',
            ]
            additionalData.asMap == [

    ss << "\n\t" << it.first << " -> "
       << DataTypeString(it.second.imported_dtype) << " "
       << it.second.shape.DebugString();
  }
  ss << "\noutputs:";
  for (auto& output : outputs) ss << " " << output;
  ss << "\ncontrol_outputs:";
  for (auto& output : control_outputs) ss << " " << output;
  ss << "\nprune_unused_nodes: " << prune_unused_nodes;
  ss << "\nconvert_legacy_fed_inputs: " << convert_legacy_fed_inputs;

// CHECK-DAG: "tf.AssignVariableOp"(%[[VAR_HANDLE_0]], %[[CST_0]])

  func.func @serving_default() -> (tensor<8xf32> {tf_saved_model.index_path = ["output"]})
    attributes {tf.entry_function = {control_outputs = "", inputs = "", outputs = "output:0"}, tf_saved_model.exported_names = ["serving_default"]} {
    %cst_0 = "tf.Const"() {device = "", value = dense<1.0> : tensor<8xf32>} : () -> tensor<8xf32>