%conv2 = "tfl.conv_2d"(%4, %5, %cst) {dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 2 : i32, stride_w = 2 : i32} : (tensor<1x112x112x32xf32>, tensor<32x3x3x3xf32>, tensor<32xf32>) -> tensor<1x56x56x32xf32>

        # One pure conv
        out = nn_ops.conv2d(
            out,
            self.conv_filters,
            strides=(1, 1, 2, 1),
            dilations=(1, 1, 1, 1),
            padding='SAME',
            data_format='NHWC',
        )

        # One fakequant attached conv
        if is_qat_model:
          out = array_ops.fake_quant_with_min_max_args(

   // Unsupported data format
   %1 = "tf.Conv2D"(%arg2, %arg1) {T = "tfdtype$DT_FLOAT", data_format = "NCHW", dilations = [1, 1, 1, 1], padding = "SAME", strides = [1, 1, 1, 1]} : (tensor<256x3x32x32xf32>, tensor<3x3x3x16xf32>) -> tensor<256x16x32x32xf32>
   // OK

// 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: }

    // Only rank size four input will be only available by the tf.Conv2D
    // operator verification.
    if (!input_type || input_type.isDynamicDim(3)) {
      return failure();
    }
    // Check if the given op is based on grouped convolution.
    // Dim size zero will be verified by the tf.Conv2D operator verification.
    if (input_type.getDimSize(3) % filter_type.getDimSize(2) != 0) {

        ),
    )
    quantization.quantize_saved_model(
        self._input_saved_model_path,
        self._output_saved_model_path,
        config,
    )

    expected_outputs = model.conv2d(input_data)

    root = load.load(self._output_saved_model_path)
    self.assertCountEqual(root.signatures.keys(), {'serving_default'})

    new_outputs = root.signatures['serving_default'](

    def "reports indirect cycle in extended configurations"() {
        def configuration = conf()
        def conf1 = conf("other")
        def conf2 = conf("other2")

        when:
        configuration.extendsFrom(conf1)
        conf1.extendsFrom(conf2)
        conf2.extendsFrom(configuration)

        then:
        thrown InvalidUserDataException
    }

    def "reports cycle introduced by setExtends"() {

			conv.getTypeIDs[n.Go[:len(n.Go)-9]] = true
		}
	}
	for i, n := range names {
		if types[i] == nil {
			continue
		}
		pos := f.NamePos[n]
		f, fok := types[i].(*dwarf.FuncType)
		if n.Kind != "type" && fok {
			n.Kind = "func"
			n.FuncType = conv.FuncType(f, pos)
		} else {
			n.Type = conv.Type(types[i], pos)
			switch n.Kind {
			case "iconst":

                maven { url "${mavenRepo.uri}" }
            }
            configurations {
                conf
                conf2
                conf3
                conf4
            }
            dependencies {
                conf 'org:a:1.0', 'org:b:1.0'
                conf2 'org:a:1.0', 'org:c:1.0'
                conf3 'org:b:1.0', 'org:c:1.0'
                conf4 'org:b:1.0', 'org:c:1.0', 'org:d:1.0'

			withMounts(map[string]string{"/home/r2lib1": "/usr/r2lib1"}),
			withEnvs(map[string]string{"r2devices": "dev1 dev2"}),
		),
	)
	testManager.podDevices.insert("pod1", "con2", resourceName1,
		constructDevices([]string{"dev3"}),
		newContainerAllocateResponse(
			withDevices(map[string]string{"/dev/r1dev3": "/dev/r1dev3"}),
			withMounts(map[string]string{"/home/r1lib1": "/usr/r1lib1"}),
		),