%zp_offset: tensor<?x2x2x1xi32>, %bias: tensor<1xi32>
  ) -> tensor<?x2x2x1xi32> {
  // CHECK-DAG: %[[conv:.*]] = mhlo.convolution
  // CHECK-DAG: %[[combined:.*]] = chlo.broadcast_add %[[zp_offset:.*]], %[[bias:.*]]
  // CHECK-DAG: %[[result:.*]] = chlo.broadcast_add %[[conv]], %[[combined]]
  // CHECK: return %[[result]]
  %0 = mhlo.convolution(%lhs, %rhs)
      dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f],

// support non-constant weights.
def ConvertTFConv2DToXLAConvOp : Pat<
  (TF_Conv2DOp:$conv
    (TF_SubOp (TF_CastOp $input, $truncate), $input_zp),
    (TF_CastOp (TF_IdentityOp $filter), $truncate1),
    $strides, $use_cudnn, $padding, $explicit_padding,
    IsDataFormatNHWC:$data_format, $dilations),
  (CreateXLAConvOpFromTFConv2DOp
    $input, $filter, $input_zp, $conv, $strides,
    $dilations, $padding, $explicit_padding),

def IsNull : Constraint<CPred<"!$0">>;

// This pattern optimizes:
//   conv/dot_general + a + b -> conv/dot_general + (a + b)
//   conv/dot_general - a - b -> conv/dot_general - (a + b)
//   conv/dot_general + a - b -> conv/dot_general + (a - b)
//   conv/dot_general - a + b -> conv/dot_general - (a - b)
foreach OpsTuple = [
    [CHLO_BroadcastAddOp, CHLO_BroadcastAddOp, CHLO_BroadcastAddOp],

	// if uint(newLen) <= uint(oldCap)
	nif := ir.NewIfStmt(base.Pos, nil, nil, nil)
	nuint := typecheck.Conv(newLen, types.Types[types.TUINT])
	scapuint := typecheck.Conv(oldCap, types.Types[types.TUINT])
	nif.Cond = ir.NewBinaryExpr(base.Pos, ir.OLE, nuint, scapuint)
	nif.Likely = true

	// then { s = s[:newLen] }

Michael Levesque-Dion <******@****.***> 1706075999 -0800

			cmp[4][op.a5] && cmp[5][op.a6] {
			p.Optab = uint16(cap(optab) - cap(ops) + i + 1)
			return op
		}
	}

	// Cannot find a case; abort.
	s := ""
	for _, a := range args {
		s += fmt.Sprintf(" %v", DRconv(int(a)))
	}
	c.ctxt.Diag("illegal combination %v%v\n", p.As, s)
	c.ctxt.Diag("prog: %v\n", p)
	return nil
}

func cmp(a int, b int) bool {
	if a == b {
		return true
	}
	switch a {

set unit=%value:~-1%
rem assume the unit is specified
set conv=%value:~0,-1%

if "%unit%" == "k" goto kilo
if "%unit%" == "K" goto kilo
if "%unit%" == "m" goto mega
if "%unit%" == "M" goto mega
if "%unit%" == "g" goto giga
if "%unit%" == "G" goto giga

rem no unit found, must be bytes; consider the whole value
set conv=%value%
rem convert to KB
set /a conv=%conv% / 1024
:kilo
rem convert to MB

// CHECK: %[[CONV:.+]] = stablehlo.convolution(%[[TRANSPOSE_0]], %[[CONST]]) dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f], window = {pad = {{\[\[}}1, 1], [1, 1]]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64} : (tensor<1x4x4x8xf32>, tensor<3x3x8x8xf32>) -> tensor<1x4x4x8xf32>
// CHECK: %[[TRANSPOSE_1:.+]] = stablehlo.transpose %[[CONV]], dims = [0, 3, 1, 2] : (tensor<1x4x4x8xf32>) -> tensor<1x8x4x4xf32>

  // CHECK: %[[QUANT_ARG_0:.*]] = stablehlo.uniform_quantize %[[ARG_0]]
  // CHECK: %[[CONV:.*]] = stablehlo.convolution(%[[QUANT_ARG_0]], %[[QUANT_CST]])
  // CHECK-NOT: stablehlo.uniform_quantize
  // CHECK: %[[DEQUANT:.*]] = stablehlo.uniform_dequantize %[[CONV]]
  // CHECK: return %[[DEQUANT]]
  %cst = stablehlo.constant dense<0.4> : tensor<2x3x3x2xf32>

// CHECK-DAG: %[[CONV:.*]] = stablehlo.convolution(%[[ARG]], %[[CONST_1]]) {{.*}} : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<1x3x2x2xf32>
// CHECK-DAG: %[[BROADCAST:.*]] = stablehlo.broadcast_in_dim %[[CONST_0]], dims = [3] : (tensor<2xf32>) -> tensor<1x3x2x2xf32>
// CHECK-DAG: %[[ADD:.*]] = stablehlo.add %[[CONV]], %[[BROADCAST]] : tensor<1x3x2x2xf32>