plugin_status.reserve(files.size());
    for (int i = 0; i < files.size(); i++)
      plugin_status.push_back(TF_NewStatus());
    result = ops_->paths_exist(filesystem_.get(), translated_names.data(),
                               files.size(), plugin_status.data());
    for (int i = 0; i < files.size(); i++) {
      status->push_back(StatusFromTF_Status(plugin_status[i]));
      TF_DeleteStatus(plugin_status[i]);
    }
  }

# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

name: Creates a GitHub Issue when a PR Rolled back via Commit to Master
on:
  push:
    branches:
      - master
      
permissions: {}

jobs:
  create-issue-on-pr-rollback:
    runs-on: ubuntu-latest
    permissions:
      contents: read

    TFE_TensorHandle* input_eager = TestScalarTensorHandle(eager_ctx, 2.0f);
    func_args.push_back(TF_CreateAbstractTensorFromEagerTensor(input_eager, s));
    ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
    input_eager = TestScalarTensorHandle(eager_ctx, 3.0f);
    func_args.push_back(TF_CreateAbstractTensorFromEagerTensor(input_eager, s));
    ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
  }

    TFE_OpAddInput(op, h, status);
    ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status);

    std::vector<TFE_TensorHandle*> result;
    result.push_back(nullptr);
    int num_retvals = 1;
    TFE_Execute(op, result.data(), &num_retvals, status);
    TFE_DeleteOp(op);
    ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status);
    ASSERT_EQ(num_retvals, 1);

    if (TF_GetCode(status.get()) != TF_OK) {
      ADD_FAILURE() << tag << " -- " << TF_Message(status.get());
      continue;
    }
    TFE_DeleteTensorHandle(hdevice);
    // Copy back to CPU
    TFE_TensorHandle* hcopy =
        TFE_TensorHandleCopyToDevice(hdevice2, ctx, kCPUDevice, status.get());
    if (TF_GetCode(status.get()) != TF_OK) {
      ADD_FAILURE() << tag << " -- " << TF_Message(status.get());

      src.flat<tstring>()(i) = data[i];
    }
    TF_Tensor* dst = TF_TensorFromTensor(src, &status);
    ASSERT_TRUE(status.ok()) << status.message();

    // Convert back to a C++ Tensor and ensure we get expected output.
    Tensor output;
    ASSERT_EQ(absl::OkStatus(), TF_TensorToTensor(dst, &output)) << line;
    ASSERT_EQ(src.NumElements(), output.NumElements()) << line;

  opts->opts.control_dependencies.push_back(oper->node.name());
}

void TF_ImportGraphDefOptionsAddReturnOutput(TF_ImportGraphDefOptions* opts,
                                             const char* oper_name, int index) {
  opts->tensor_id_data.push_back(oper_name);
  const string& oper_name_str = opts->tensor_id_data.back();
  opts->opts.return_tensors.emplace_back(oper_name_str, index);
}

  return wrap(unwrap(o)->outputs[i]);
}
void TF_OutputListPushBack(TF_OutputList* o, TF_AbstractTensor* tensor,
                           TF_Status* s) {
  unwrap(o)->outputs.push_back(unwrap(tensor));
}

void TF_AbstractOpSetOpType(TF_AbstractOp* op, const char* const op_type,
                            TF_Status* s) {
  tsl::Set_TF_Status_from_Status(
      s, unwrap(op)->Reset(op_type,

  forward_op_->inputs.push_back(input);
  return absl::OkStatus();
}
Status AddInputList(AbstractOperation* op_,
                    absl::Span<AbstractTensorHandle* const> inputs,
                    ForwardOperation* forward_op_) {
  TF_RETURN_IF_ERROR(op_->AddInputList(inputs));
  for (auto input : inputs) {
    forward_op_->inputs.push_back(input);
  }
  return absl::OkStatus();
}

};

TEST_P(CppGradients, TestIdentityNGrad) {
  // This test is interesting because the current implementation of GradientTape
  // would return [0, 1] whereas we use build_default_zeros_grads=false here
  // so we get back [nullptr, 1].

  AbstractTensorHandlePtr x1;
  {
    AbstractTensorHandle* x1_raw = nullptr;
    status_ = TestScalarTensorHandle<float, TF_FLOAT>(
        immediate_execution_ctx_.get(), 1.0f, &x1_raw);