EXPECT_EQ(feed, add_in_0.oper);
  EXPECT_EQ(0, add_in_0.index);
  TF_Output add_in_1 = TF_OperationInput(TF_Input{add, 1});
  EXPECT_EQ(three, add_in_1.oper);
  EXPECT_EQ(0, add_in_1.index);
  EXPECT_EQ(0, TF_OperationOutputNumConsumers(TF_Output{add, 0}));
  EXPECT_EQ(0, TF_OperationNumControlInputs(add));
  EXPECT_EQ(0, TF_OperationNumControlOutputs(add));

    return absl::OkStatus();
  }
  ~SparseSoftmaxCrossEntropyWithLogitsGradientFunction() override {}

 private:
  vector<AbstractTensorHandle*> forward_outputs_;
};

// TODO(vnvo2409): Add python test
class BiasAddGradientFunction : public GradientFunction {
 public:
  explicit BiasAddGradientFunction(AttrBuilder f_attrs)
      : forward_attrs_(f_attrs) {}

  Status Compute(AbstractContext* ctx,

def _running_from_pip_package():
  return any(
      _current_file_location.startswith(dir_) for dir_ in _site_packages_dirs)

if _running_from_pip_package():
  # TODO(gunan): Add sanity checks to loaded modules here.

  # Load first party dynamic kernels.
  _tf_dir = _os.path.dirname(_current_file_location)
  _kernel_dir = _os.path.join(_tf_dir, "core", "kernels")
  if _os.path.exists(_kernel_dir):

        return;
      }
      // We have a convoluted scheme here: Using the C++ graph construction API
      // to add potentially many nodes to the graph without running the checks
      // (such as uniqueness of the names of nodes) we run with other functions
      // that add a node to the graph (like TF_FinishOperation).
      if (!g->name_map.insert(std::make_pair(n->name(), n)).second) {

    bazel by doing as follows:

    ```bash
    export flags="--config=opt -k"
    ```

    If the tests are to be run on the GPU, add CUDA paths to LD_LIBRARY_PATH and
    add the `cuda` option flag

    ```bash
    export LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH"

    *   Add batch_dims argument to tf.gather.
    *   Add support for `add_metric` in the graph function mode.
    *   Add C++ Gradient for BatchMatMulV2.
    *   Added tf.random.binomial
    *   Added gradient for SparseToDense op.
    *   Add legacy string flat hash map op kernels
    *   Add a ragged size op and register it to the op dispatcher
    *   Add broadcasting support to tf.matmul.

  }

  Status Compute(AbstractContext* ctx,
                 absl::Span<AbstractTensorHandle* const> grad_outputs,
                 absl::Span<AbstractTensorHandle*> grad_inputs) override {
    // TODO(vnvo2409): Add control dependency
    /* Given upstream grad U and a Log1p op: Y = log(1 + X), the gradients are:
     *
     *    dX = U / (1 + X)
     *
     */

    AbstractTensorHandle* upstream_grad = grad_outputs[0];

"$python" -m pip install *.whl $TFCI_PYTHON_VERIFY_PIP_INSTALL_ARGS
if [[ "$TFCI_WHL_IMPORT_TEST_ENABLE" == "1" ]]; then
  "$python" -c 'import tensorflow as tf; t1=tf.constant([1,2,3,4]); t2=tf.constant([5,6,7,8]); print(tf.add(t1,t2).shape)'
  "$python" -c 'import sys; import tensorflow as tf; sys.exit(0 if "keras" in tf.keras.__name__ else 1)'
fi
# VERY basic check to ensure the [and-cuda] package variant is installable.

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

void TFE_MonitoringSamplerCellAdd(TFE_MonitoringSamplerCell* cell,
                                  double value) {
  cell->cell.Add(value);
}

void TFE_MonitoringSamplerCellValue(TFE_MonitoringSamplerCell* cell,
                                    TF_Buffer* buf) {
  string content;
  cell->cell.value().SerializeToString(&content);

)

selects.config_setting_group(
    name = "api_indexable",
    match_any = [":api_indexable_flag"] + ADDITIONAL_API_INDEXABLE_SETTINGS,
    visibility = ["//visibility:public"],
)

# DO NOT ADD ANY NEW EXCEPTIONS TO THIS LIST!
# Instead, please use public APIs or public build rules TF provides.
# If you need functionality that is not exposed, we will work with you to expand our public APIs.