// Check that Conv2D computed in NCHW format, and all redundant transpose
  // operations removed from the function.

  // CHECK: %[[CONV:[0-9]*]] = "tf.Conv2D"(%arg0, %arg1)
  // CHECK-SAME: data_format = "NCHW"
  // CHECK-SAME: -> tensor<1x8x32x32xf32>

  // CHECK: return %[[CONV]]

  func.return %4 : tensor<1x8x32x32xf32>

// THE SOFTWARE.

package s390x

import (
	"cmd/internal/obj"
	"fmt"
)

func init() {
	obj.RegisterRegister(obj.RBaseS390X, REG_R0+1024, rconv)
	obj.RegisterOpcode(obj.ABaseS390X, Anames)
}

func rconv(r int) string {
	if r == 0 {
		return "NONE"
	}
	if r == REGG {
		// Special case.
		return "g"
	}
	if REG_R0 <= r && r <= REG_R15 {
		return fmt.Sprintf("R%d", r-REG_R0)

  %b = arith.constant dense<0.0> : tensor<3xf32>
  %conv = "tfl.conv_2d"(%arg0, %w, %b) {dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "RELU", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32} : (tensor<1x5x5x3xf32>, tensor<3x3x3x3xf32>, tensor<3xf32>) -> tensor<1x5x5x3xf32>
  func.return %conv : tensor<1x5x5x3xf32>

  // CHECK: %[[weight:.*]] = arith.constant dense<3.000000e+00> : tensor<3x3x3x3xf32>
  // CHECK: %[[bias:.*]] = arith.constant dense<[1.500000e+00, 3.000000e+00, 4.500000e+00]>
  // CHECK: %[[conv:.*]] = "tfl.conv_2d"(%arg0, %[[weight]], %[[bias]])
  // CHECK: return %[[conv]] : tensor<256x8x7x3xf32>

// CHECK-NEXT: %[[cst2:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<32x!quant.uniform<i32:f32, 1.7052092479439231E-4>>, value = dense<0> : tensor<32xi32>}> : () -> tensor<32x!quant.uniform<i32:f32, 1.7052092479439231E-4>>

  // CHECK: %[[CONV:.+]] = mhlo.convolution(%[[INPUT]], %[[NEW_FILTER]]) dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f], window = {stride = [1, 1], pad = {{\[\[}}0, 0], [0, 0]], rhs_dilate = [1, 1]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64} : (tensor<?x256x256x3xf32>, tensor<1x1x3x2xf32>) -> tensor<?x256x256x2xf32>
  // CHECK: %[[SHAPE:.+]] = shape.shape_of %[[CONV]] : tensor<?x256x256x2xf32> -> tensor<4xindex>

  Pat<(TF_MulOp:$mul (TF_Conv2DOp:$conv $input,
                          (Arith_ConstantOp:$filter F32ElementsAttr:$filter_value),
                          $strides, $use_cudnn, $padding, $explicit_padding,
                          IsDataFormatNHWC:$data_format, $dilations),
                     (Arith_ConstantOp:$multiplier F32ElementsAttr:$mul_value)),
// TODO(karimnosseir): Add check for $conv is of rank 4.
      (TF_Conv2DOp $input,

// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

// Issue 16317: cmd/compile: internal compiler error:
//              unhandled OCONV INT -> TUNSAFEPTR

// CHECK: %[[CONV:.+]] = stablehlo.convolution(%[[ARG1]], %[[ARG2]])
// CHECK-SAME: (tensor<1x3x4x3xf32>, tensor<2x3x3x2x!quant.uniform<i8<-127:127>:f32, 0.0023622048182750312>>) -> tensor<1x3x4x2xf32>
// CHECK: return %[[CONV]]

// -----

// Test that per-channel weight-only quantized dot_general op is produced when

        String expectedValue = 'somevalue'

        when:
        project.convention.plugins.test = new TestConvention()
        project.conv = expectedValue

        then:
        project.conv == expectedValue
        project.convention.plugins.test.conv == expectedValue
        child1.conv == expectedValue
    }

    def setPropertyAndPropertyMissingWithProjectAndConventionProperty() {
        given: