import static java.util.Collections.emptyList;

/**
 * Converts progress events sent from the tooling provider to the tooling client to the corresponding event types available on the public Tooling API, and broadcasts the converted events to the
 * matching progress listeners. This adapter handles all the different incoming progress event types (except the original logging-derived progress listener).
 */

        config.resolve()

        then:
        parent.observedState == ConfigurationInternal.InternalState.GRAPH_RESOLVED
    }

    def "resolving configuration puts it into the right state and broadcasts events"() {
        def listenerBroadcaster = Mock(AnonymousListenerBroadcast)
        def listener = Mock(DependencyResolutionListener)
        def config

        when:
        config = conf("conf")

          {"MISC",
           // clang-format off
     {"ApproxTopK", "BroadcastTo", "ExpandDims", "Fill", "NoOp",
      "Range", "Rank", "Reshape", "Shape", "ShapeN", "Size", "Squeeze",
      "Transpose", "ZerosLike", "OnesLike", "BiasAdd" /*PW + Broadcast*/,
      "BroadcastArgs", "BroadcastGradientArgs", "OneHot", "Concat", "ConcatV2",

  friend class internal::NoExecDeathTest;
  friend class internal::TestEventListenersAccessor;
  friend class internal::UnitTestImpl;

  // Returns repeater that broadcasts the TestEventListener events to all
  // subscribers.
  TestEventListener* repeater();

  // Sets the default_result_printer attribute to the provided listener.

  friend class internal::NoExecDeathTest;
  friend class internal::TestEventListenersAccessor;
  friend class internal::UnitTestImpl;

  // Returns repeater that broadcasts the TestEventListener events to all
  // subscribers.
  TestEventListener* repeater();

  // Sets the default_result_printer attribute to the provided listener.

removing MLIR or XLA specific attributes that are not legal in TensorFlow
graph.
### `-tf-batch-matmul-to-tf-einsum`

_Replace TF BatchMatMul op by TF Einsum op._

### `-tf-broadcast-fold`

_Fold explicit broadcasts into the following operations if they support implicit broadcasting on their operand._

### `-tf-canonicalize-compile-and-replicate-attributes`

_Canonicalize compilation and replication attributes._

  // It is safe to perform this transform here because-
  // the shapes of `pre_broadcast` and `dim` must be broadcast
  // compatible for the `broadcast_to` op to be valid.
  // And considering, `shape(post_broadcast)` == `shape(%input1)`,
  // `post_broadcast` is broadcast compatible with `input1`.
  def FuseBroadcastInto#SelectOp : Pat<
    (SelectOp

}

func.func @broadcast_to_i16_low_dim(%input: tensor<3xi16>, %shape: tensor<2xi32>) -> tensor<3x3xi16> {
  %0 = "tf.BroadcastTo"(%input, %shape) : (tensor<3xi16>, tensor<2xi32>) -> tensor<3x3xi16>
  func.return %0: tensor<3x3xi16>

// CHECK-LABEL: broadcast_to_i16_low_dim
// CHECK:    %0 = "tf.BroadcastTo"(%arg0, %arg1) : (tensor<3xi16>, tensor<2xi32>) -> tensor<3x3xi16>
// CHECK:    return %0 : tensor<3x3xi16>
}

  grad_outputs->push_back(Mul(scope, Neg(scope, y), grad_inv));
  return scope.status();
}
REGISTER_GRADIENT_OP("Atan2", Atan2Grad);

// BinaryGradCommon handles the setup for binary ops that broadcast
// their inputs.
Status BinaryGradCommon(const Scope& scope, const Operation& op,
                        std::vector<Output>* grad_outputs, const Output& gx_1,
                        const Output& gx_2) {

	metrics.SchedulerQueueIncomingPods.WithLabelValues("active", PodAdd).Inc()
	p.addNominatedPodUnlocked(logger, pInfo.PodInfo, nil)
	p.cond.Broadcast()

	return nil
}

// Activate moves the given pods to activeQ iff they're in unschedulablePods or backoffQ.
func (p *PriorityQueue) Activate(logger klog.Logger, pods map[string]*v1.Pod) {
	p.lock.Lock()