assignees.push('rino20', 'yyoon', 'lenscloth');
  }

  const resp_label = await github.rest.issues.addLabels({
    issue_number: context.issue.number,
    owner: context.repo.owner,
    repo: context.repo.repo,
    labels: labels
  });
  if (resp_label.status >= 400) {
    console.log(resp_label);
    throw "Error adding labels to PR";
  }
  if (assignees.length > 0) {

          days-before-issue-close: 7
          stale-issue-label: "stale"
          # reason for closed the issue default value is not_planned
          close-issue-reason: completed
          # List of labels to remove when issues/PRs unstale. 
          labels-to-remove-when-unstale: 'stat:awaiting response'
          only-labels: "stat:awaiting response"
          stale-issue-message: > 

// "description", "label");
// and then incremented that counter for various values of label:
// streamz->GetCell("label-value")->IncrementBy(1);
//
// The test code can then read and test the value of that counter:
//
// auto* reader = TFE_MonitoringNewCounterReader("name");
// test();
// int64_t value = TFE_MonitoringReadCounter1(reader, "label-value");

// Opaque handle to a reader.

    attributes:
      label: Python version
      placeholder: e.g., 3.9
  - type: input
    id: Bazel
    attributes:
      label: Bazel version
      description: If compiling from source
  - type: input
    id: Compiler
    attributes:
      label: GCC/compiler version
      description: If compiling from source
  - type: input
    id: Cuda
    attributes:
      label: CUDA/cuDNN version
  - type: input
    id: Gpu

      static_cast<void*>(gauge->gauge->GetCell(label1, label2)));
}

TFE_MonitoringStringGauge3* TFE_MonitoringNewStringGauge3(
    const char* name, TF_Status* status, const char* description,
    const char* label1, const char* label2, const char* label3) {
  auto* result = new TFE_MonitoringStringGauge3(
      {name, description, label1, label2, label3});

    // Grad for Softmax Input
    TF_RETURN_IF_ERROR(BroadcastMul(
        ctx, grad_outputs[0], forward_outputs_[1],
        grad_inputs.subspan(0, 1)));  // upstream_grad * local softmax grad

    // Grad for labels is null
    grad_inputs[1] = nullptr;
    return absl::OkStatus();
  }
  ~SparseSoftmaxCrossEntropyWithLogitsGradientFunction() override {}

 private:
  vector<AbstractTensorHandle*> forward_outputs_;
};

---
name: TensorFlow Lite in Play Services issue
about: Use this template for issues with TensorFlow Lite in Google Play Services
labels: 'comp:lite-in-play-services'

---

**System information**
- Android Device information (use `adb shell getprop ro.build.fingerprint`
  if possible):
- TensorFlow Lite in Play Services SDK version (found in `build.gradle`):
- Google Play Services version

    counter = std::make_unique<
        ::tensorflow::monitoring::testing::CellReader<int64_t>>(name);
  }
  template <typename... LabelType>
  int64_t Read(const LabelType&... labels);
  std::unique_ptr<::tensorflow::monitoring::testing::CellReader<int64_t>>
      counter;
};

---
name: TensorFlow Lite Converter Issue
about: Use this template for reporting issues during model conversion to TFLite
labels: 'TFLiteConverter'

---

### 1. System information

- OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
- TensorFlow installation (pip package or built from source):
- TensorFlow library (version, if pip package or github SHA, if built from source):

### 2. Code

predictions), and force use of named args. * tf.nn.rnn_cell.* and most functions
in tf.nn.rnn.* (with the exception of dynamic_rnn and raw_rnn) are temporarily
in tf.contrib.rnn. They will be moved back into core for TF 1.2. *
`tf.nn.sampled_softmax_loss` and `tf.nn.nce_loss` have both changed their API
such that you need to switch the `inputs, labels` to `labels, inputs`