z.form = zero
		z.neg = false
		return
	}
	// len(z.mant) > 0

	z.setExpAndRound(ex+int64(len(z.mant))*_W-fnorm(z.mant), 0)
}

// z = x * y, ignoring signs of x and y for the multiplication
// but using the sign of z for rounding the result.
// x and y must have a non-empty mantissa and valid exponent.
func (z *Float) umul(x, y *Float) {
	if debugFloat {
		validateBinaryOperands(x, y)
	}

    TF_DeleteAbstractOp(add_op);

    // Extract the resulting tensor.
    add_output2 = TF_OutputListGet(add_outputs, 0);
    TF_DeleteOutputList(add_outputs);
  }

  // 3rd Output will be Matrix Multiplication of add_output1 and add_output2
  TF_AbstractTensor* mm_output;
  {
    // Build an abstract operation, inputs and output.
    auto* mm_op = TF_NewAbstractOp(graph_ctx);
    TF_AbstractOpSetOpType(mm_op, "MatMul", s);

//go:build !purego

// This is an optimized implementation of AES-GCM using AES-NI and CLMUL-NI
// The implementation uses some optimization as described in:
// [1] Gueron, S., Kounavis, M.E.: Intel® Carry-Less Multiplication
//     Instruction and its Usage for Computing the GCM Mode rev. 2.02
// [2] Gueron, S., Krasnov, V.: Speeding up Counter Mode in Software and
//     Hardware

#include "textflag.h"

#define B0 X0

		rgba64   *image.RGBA64
		nrgba64  *image.NRGBA64
		img      image.Image
	)
	width, height := d.width, d.height
	if d.interlace == itAdam7 && !allocateOnly {
		p := interlacing[pass]
		// Add the multiplication factor and subtract one, effectively rounding up.
		width = (width - p.xOffset + p.xFactor - 1) / p.xFactor
		height = (height - p.yOffset + p.yFactor - 1) / p.yFactor

      """A model with two matmul ops."""

      @def_function.function
      def matmul(self, input_tensor: core.Tensor) -> Mapping[str, core.Tensor]:
        """Performs a matrix multiplication.

        Args:
          input_tensor: Input tensor to matmul with the filter.

        Returns:
          A 'output' -> output tensor mapping
        """

                            const bool t_x, const bool t_y,
                            std::function<Output(Output, Output)> mul_fn) {
    TF_ASSERT_OK(root_.status());
    // Generate random (but compatible) shapes for matrix multiplication.
    std::vector<TensorShape> shapes;
    RandMatMulShapes(is_x_batch, is_y_batch, t_x, t_y, &shapes);
    TensorShape x_shape = shapes[0];
    TensorShape y_shape = shapes[1];
    TensorShape z_shape = shapes[2];

        return self.has_bias() and self.bias_size != self.filters.shape[-1]

      @def_function.function
      def matmul(self, input_tensor: core.Tensor) -> Mapping[str, core.Tensor]:
        """Performs a matrix multiplication.

        Depending on self.has_bias and self.activation_fn, it may add a bias
        term or
        go through the activaction function.

        Args:

(RISBGZ x {r}) && r == s390x.NewRotateParams(32, 63, 0) => (MOVWZreg x)

// Use sign/zero extend instead of ANDW.
(ANDWconst [0x00ff] x) => (MOVBZreg x)
(ANDWconst [0xffff] x) => (MOVHZreg x)

// Strength reduce multiplication to the sum (or difference) of two powers of two.
//
// Examples:
//     5x -> 4x + 1x
//    10x -> 8x + 2x
//   120x -> 128x - 8x
//  -120x -> 8x - 128x
//

// Merge negation into fused multiply-add and multiply-subtract.
//
// Key:
//
//   [+ -](x * y [+ -] z).
//    _ N         A S
//                D U
//                D B
//
// Note: multiplication commutativity handled by rule generator.
(F(MADD|NMADD|MSUB|NMSUB)S neg:(FNEGS x) y z) && neg.Uses == 1 => (F(NMSUB|MSUB|NMADD|MADD)S x y z)

//
// where the signs of u0, u1, v0, v1 are given by even
// For even == true: u0, v1 >= 0 && u1, v0 <= 0
// For even == false: u0, v1 <= 0 && u1, v0 >= 0
// q, r, s, t are temporary variables to avoid allocations in the multiplication.
func lehmerUpdate(A, B, q, r, s, t *Int, u0, u1, v0, v1 Word, even bool) {

	t.abs = t.abs.setWord(u0)
	s.abs = s.abs.setWord(v0)
	t.neg = !even
	s.neg = even

	t.Mul(A, t)
	s.Mul(B, s)