std::unique_ptr<monitoring::CollectedMetrics> metrics =
      collection_registry->CollectMetrics(options);

  EXPECT_EQ("test/gauge", metrics->point_set_map.at("test/gauge")->metric_name);
  EXPECT_EQ(1,
            metrics->point_set_map.at("test/gauge")->points.at(0)->int64_value);

  TFE_MonitoringIntGaugeCellSet(cell, 5);
  metrics = collection_registry->CollectMetrics(options);
  EXPECT_EQ(5,

    TFE_MonitoringCounterCell* cell);

// APIs for Counter without label.
typedef struct TFE_MonitoringCounter0 TFE_MonitoringCounter0;
// Returns a new Counter metric object. The caller should manage lifetime of
// the object. Using duplicate metric name will crash the program with fatal
// error.
TF_CAPI_EXPORT extern TFE_MonitoringCounter0* TFE_MonitoringNewCounter0(
    const char* name, TF_Status* status, const char* description);

    runs-on: [self-hosted, linux, ARM64]
    strategy:
      matrix:
        pyver: ['3.10']
    steps:
      - name: Stop old running containers (if any)
        shell: bash
        run: |
          running_containers=$(docker ps -q) && \

  /* Want to test simple MatMul example with abstract tensors:
    [[1,2],   *  [[5,6],   =   [[19,22],
     [3,4]]       [7,8]]        [43,50]]
  */

  // Build 1st Matrix.
  int64_t dims[] = {2, 2};  // Matrices will be 2 x 2
  int num_dims = sizeof(dims) / sizeof(dims[0]);

  float vals1[] = {1.0f, 2.0f, 3.0f, 4.0f};

    EXPECT_NE(TF_OK, TF_GetCode(status));
  } else {
    TF_Tensor* t = TFE_TensorHandleResolve(retvals[0], status);
    EXPECT_NE(TF_OK, TF_GetCode(status));
    EXPECT_EQ(nullptr, t);
    const char* msg = "Matrix size-incompatible: In[0]: [2,2], In[1]: [3,2]";
    EXPECT_TRUE(strstr(TF_Message(status), msg) != nullptr)
        << TF_Message(status);
    // Since error is not cleared, the following copy with correct device will

### Major Features and Improvements

*   Added F-Score metrics `tf.keras.metrics.FBetaScore`, `tf.keras.metrics.F1Score`, and `tf.keras.metrics.R2Score`.
*   Added activation function `tf.keras.activations.mish`.

_metrics = _KerasLazyLoader(globals(), submodule="metrics", name="metrics")
_optimizers = _KerasLazyLoader(
    globals(), submodule="optimizers", name="optimizers")
_initializers = _KerasLazyLoader(
    globals(), submodule="initializers", name="initializers")
setattr(_current_module, "losses", _losses)
setattr(_current_module, "metrics", _metrics)
setattr(_current_module, "optimizers", _optimizers)

permissions:
  contents: read

jobs:
  build:
    if: github.repository == 'tensorflow/tensorflow' # Don't do this in forks
    runs-on: [self-hosted, linux, ARM64]
    strategy:
      matrix:
        pyver: ['3.10']
    steps:
      - name: Stop old running containers (if any)
        shell: bash
        run: |
          running_containers=$(docker ps -q) && \

permissions:
  contents: read

jobs:
  build:
    if: github.repository == 'tensorflow/tensorflow' # Don't do this in forks
    runs-on: [self-hosted, linux, ARM64]
    strategy:
      fail-fast: false
      matrix:
        pyver: ['3.9', '3.10', '3.11', '3.12']
    steps:
      - name: Stop old running containers (if any)
        shell: bash
        run: |
          running_containers=$(docker ps -q) && \

-   Continuous jobs (Run on every GitHub commit)
    -   Uses `pycpp.sh`
-   Presubmit jobs (Run on every GitHub PR)
    -   Uses `pycpp.sh`, `code_check_changed_files.sh`

These "env" files match up with an environment matrix that roughly covers:

-   Different Python versions
-   Linux, MacOS, and Windows machines (these pool definitions are internal)
-   x86 and arm64
-   CPU-only, or with NVIDIA CUDA support (Linux only), or with TPUs