// CHECK-DAG: %[[MULTIPLIER:.+]] = mhlo.multiply %[[VARIANCE_EPS_RSQRT]], %[[SCALE]] : tensor<256xf32>
  // CHECK-DAG: %[[MUL_MEAN:.+]] = mhlo.multiply %[[MULTIPLIER]], %[[MEAN]] : tensor<256xf32>
  // CHECK-DAG: %[[RHS:.+]] = mhlo.subtract %[[OFFSET]], %[[MUL_MEAN]] : tensor<256xf32>

    if (!epsilon) {
      return failure();
    }

    // Compute multiplier = scale / sqrt(variance + epsilon)
    Value multiplier = rewriter.create<mhlo::AddOp>(
        bn_op.getLoc(), bn_op.getVariance(), epsilon);
    multiplier = rewriter.create<mhlo::RsqrtOp>(bn_op.getLoc(), multiplier);
    multiplier = rewriter.create<mhlo::MulOp>(bn_op.getLoc(), multiplier,
                                              bn_op.getScale());

                    @Test
                    public void testMultiply() {
                        Multiplier multiplier = new Multiplier();
                        Assert.assertEquals(1, multiplier.multiply(1, 1));
                        Assert.assertEquals(4, multiplier.multiply(2, 2));
                        Assert.assertEquals(2, multiplier.multiply(1, 2));
                    }
                }
            """

    return mul_op.getResult();
  }
  auto q_op = dq_op.getArg().getDefiningOp<quantfork::QuantizeCastOp>();
  if (!q_op) return {};

  Value float_value = q_op.getArg();
  Value new_value = builder.create<TF::MulOp>(loc, float_value, multiplier);
  auto new_value_type = mlir::cast<TensorType>(new_value.getType());

  // Get multiplier value in double.

                    @Test
                    public void testMultiply() {
                        Multiplier multiplier = new Multiplier();
                        Assert.assertEquals(1, multiplier.multiply(1, 1));
                        Assert.assertEquals(4, multiplier.multiply(2, 2));
                        Assert.assertEquals(2, multiplier.multiply(1, 2));
                    }
                }
            """

	if term.Matches(pod, nsLabels) {
		if tpValue, tpValueExist := node.Labels[term.TopologyKey]; tpValueExist {
			if m[term.TopologyKey] == nil {
				m[term.TopologyKey] = make(map[string]int64)
			}
			m[term.TopologyKey][tpValue] += int64(weight * multiplier)
		}
	}
}

                        jar {
                            attributes.attribute(color, 'blue')
                        }
                    }
                }
            }

            abstract class Multiplier implements TransformAction<TransformParameters.None> {
                @InputArtifact
                abstract Provider<FileSystemLocation> getInputArtifact()

                void transform(TransformOutputs outputs) {

	// The number of references to these ReadWriteOncePod volumes by scheduled pods.
	conflictingPVCRefCount int
}

func (s *preFilterState) updateWithPod(podInfo *framework.PodInfo, multiplier int) {
	s.conflictingPVCRefCount += multiplier * s.conflictingPVCRefCountForPod(podInfo)
}

func (s *preFilterState) conflictingPVCRefCountForPod(podInfo *framework.PodInfo) int {
	conflicts := 0

    if (function_name.contains("depthwise_conv2d")) {
      // Uniform Quantized op requires weights of tf.DepthwiseConv2dNative to
      // be transformed from [H,W,C,M] to [H,W,1,CxM] where
      // H=height,W=width,C=channel,M=multiplier. Therefore, a reshape op is
      // inserted between the constant op and the function op so that the
      // constant is safely transformed for the multi-use cases as well. Note

    // This class can make no hard guarantees.  The methods in this class are inherently flaky, but
    // we try hard to make them robust in practice.  We could additionally try to add in a system
    // load timeout multiplier.  Or we could try to use a CPU time bound instead of wall clock time
    // bound.  But these ideas are harder to implement.  We do not try to detect or handle a
    // user-specified -XX:+DisableExplicitGC.
    //