for (int i = 0; i < ninputs; ++i) {
    input_names[i] = c_input_names[i];
  }
  for (int i = 0; i < noutputs; ++i) {
    output_names[i] = c_output_names[i];
  }
  for (int i = 0; i < ntargets; ++i) {
    target_oper_names[i] = c_target_oper_names[i];
  }
  string new_handle;
  status->status = s->session->PRunSetup(input_names, output_names,

// See TF_SessionRun() above.
TF_CAPI_EXPORT extern void TF_Run(TF_DeprecatedSession*,
                                  const TF_Buffer* run_options,
                                  const char** input_names, TF_Tensor** inputs,
                                  int ninputs, const char** output_names,
                                  TF_Tensor** outputs, int noutputs,

                                    " into function '", fn_name, "'");

    input_tensors->emplace_back(node, idx);

    const auto& iter = input_nodes->find(node);
    if (iter == input_nodes->end()) {
      input_nodes->insert({node, {idx}});
    } else {
      auto& indices = iter->second;
      if (std::find(indices.begin(), indices.end(), idx) != indices.end()) {

    for (String invalid : invalidCases) {
      assertFalse(invalid, InternetDomainName.isValid(invalid));
    }
  }

  public void testToString() {
    for (String inputName : SOMEWHERE_UNDER_PS) {
      InternetDomainName domain = InternetDomainName.from(inputName);

      /*
       * We would ordinarily use constants for the expected results, but
       * doing it by derivation allows us to reuse the test case definitions

            `Model.predict_step`.
        *   SavedModel uses its own `Model._saved_model_inputs_spec` attr now
            instead of relying on `Model.inputs` and `Model.input_names`, which
            are no longer set for subclass Models. This attr is set in eager,
            `tf.function`, and graph modes. This gets rid of the need for users

  // Retrieve the regression signature from meta graph def.
  const auto signature_def_map = metagraph_def.signature_def();
  const auto signature_def = signature_def_map.at("regress_x_to_y");

  const string input_name =
      signature_def.inputs().at(tensorflow::kRegressInputs).name();
  const string output_name =
      signature_def.outputs().at(tensorflow::kRegressOutputs).name();