// 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>

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-DAG: %[[cst:.*]] = "tf.Const{{.*}} dense<8.000000e+00> : tensor<3x3x3x16xf32>
// CHECK-DAG: %[[cst_0:.*]] = "tf.Const{{.*}} dense<1.200000e+01> : tensor<16xf32>
// CHECK-NEXT: %[[conv:.*]] = "tf.Conv2D"(%arg0, %[[cst]])
// CHECK-NEXT: %[[bias:.*]] = "tf.AddV2"(%[[conv]], %[[cst_0]])
// CHECK-NEXT: return %[[bias]] : tensor<256x8x7x16xf32>
}

// CHECK-LABEL: convaddv2mul

  // 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>

        def conf1 = dependencyScope("conf1")
        def conf2 = dependencyScope("conf2")
        def conf3 = dependencyScope("conf3", [conf1, conf2])
        resolvable("child1", [conf3])
        resolvable("child2", [conf1])

        and:
        conf1.getDependencies().add(files1)
        conf2.getDependencies().add(files2)
        conf3.getDependencies().add(files3)

        expect:

// CHECK: %[[DEQUANTIZE:.*]] = "quantfork.dcast"(%[[QUANTIZE]])
// CHECK: %[[CONV:.*]] = "tf.Conv2D"(%arg0, %[[DEQUANTIZE]])
// CHECK: return %[[CONV]]
}

// CHECK-LABEL: perChannelFakeQuantWithConv2D
func.func @perChannelFakeQuantWithConv2D(tensor<256x32x32x3xf32>) -> (tensor<256x8x7x16xf32>) {
^bb0(%arg: tensor<256x32x32x3xf32>) :

// 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>

// CHECK: }

// CHECK-LABEL: private @composite_conv_with_bias_dynamic_fn_1
// CHECK: %[[CONV:.*]] = stablehlo.convolution(%arg0, %arg1)
// CHECK: %[[SHAPE_OF:.*]] = shape.shape_of %[[CONV]]
// CHECK: %[[DYNAMIC_BROADCAST_IN_DIM:.*]] = stablehlo.dynamic_broadcast_in_dim %arg2, %[[SHAPE_OF]]
// CHECK: %[[ADD:.*]] = stablehlo.add %[[CONV]], %[[DYNAMIC_BROADCAST_IN_DIM]]
// CHECK: return %[[ADD]] : tensor<?x28x28x16xf32>
// CHECK: }

				cmplw := ir.Node(ir.NewIndexExpr(base.Pos, cmpl, ir.NewInt(base.Pos, i)))
				cmplw = typecheck.Conv(cmplw, elemType) // convert to unsigned
				cmplw = typecheck.Conv(cmplw, convType) // widen
				cmprw := ir.Node(ir.NewIndexExpr(base.Pos, cmpr, ir.NewInt(base.Pos, i)))
				cmprw = typecheck.Conv(cmprw, elemType)
				cmprw = typecheck.Conv(cmprw, convType)
				// For code like this:  uint32(s[0]) | uint32(s[1])<<8 | uint32(s[2])<<16 ...

// CHECK: %[[cast:.*]] = "tf.Cast"(%[[cst]]) <{Truncate = false}> : (tensor<2x3x3x2xbf16>) -> tensor<2x3x3x2xf32>
// CHECK: %[[conv:.*]] = "tf.Conv2D"(%arg0, %[[cast]])
// CHECK: %[[identity:.*]] = "tf.IdentityN"(%[[conv]]) {device = ""} : (tensor<1x3x2x2xf32>) -> tensor<1x3x2x2xf32>
// CHECK: return %[[identity]] : tensor<1x3x2x2xf32>