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());

    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.

		}
	}
}

func TestEncoderDecoder(t *testing.T) {
	for _, multiplier := range []int{1, 128, 192} {
		for _, test := range encDecTests {
			input := bytes.Repeat(test.dec, multiplier)
			output := strings.Repeat(test.enc, multiplier)

			var buf bytes.Buffer
			enc := NewEncoder(&buf)

                    @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));
                    }
                }
            """

                        parameters.multiplier.set(2)
                    }
                    registerTransform(MakeColor) {
                        from.attribute(color, 'red')
                        to.attribute(color, 'green')
                        parameters.targetColor.set('green')
                        parameters.multiplier.set(1)
                    }
                }
            }

    // have a corresponding 'depth_multiplier' attribute; the multiplier is the
    // fourth dimension in the 4-D filter tensor. We query the multiplier from
    // tf.DepthwiseConv2dNative and set it as the attribute value accordingly.
    auto multiplier =
        mlir::cast<RankedTensorType>(filter.getType()).getDimSize(3);

    filter = legalizeFilter(rewriter, loc, filter);

// creation and use, no reproducibility, no concurrency safety, just the
// necessary methods, optimized for speed.

var globalInc atomic.Uint64 // PCG stream

const multiplier uint64 = 6364136223846793005

// pcgRand is a PRNG. It should not be copied or shared. No Rand methods are
// concurrency safe.
type pcgRand struct {

	// https://github.com/imneme/pcg-cpp/blob/428802d1a5/include/pcg_random.hpp#L161
	//
	// Numpy's PCG multiplies by the 64-bit value cheapMul
	// instead of the 128-bit value used here and in the official PCG code.
	// This does not seem worthwhile, at least for Go: not having any high
	// bits in the multiplier reduces the effect of low bits on the highest bits,
	// and it only saves 1 multiply out of 3.

	EnvMaxWait       = "MINIO_SCANNER_MAX_WAIT"
	EnvMaxWaitLegacy = "MINIO_CRAWLER_MAX_WAIT"
)

// Config represents the heal settings.
type Config struct {
	// Delay is the sleep multiplier.
	Delay float64 `json:"delay"`

	// Sleep always or based on incoming S3 requests.
	IdleMode int32 // 0 => on, 1 => off

	// Alert upon this many excess object versions
	ExcessVersions int64 // 100

                $w_factor, TFL_AF_None, $padding, $stride_h, $stride_w,
                $multiplier)),
    (TFL_DepthwiseConv2DOp $input, $filter, $bias, $h_factor, $w_factor,
        ActFnAttr, $padding, $stride_h, $stride_w, $multiplier),
    [(HasOneUse $conv_out)]>;
}

multiclass FuseActFnIntoPoolOpPat<Op ActFnOp, ConstantStrAttr ActFnAttr> {