private Configuration conf1
    private Configuration conf2

    def setup() {
        task = TestUtil.createTask(DependencyReportTask.class, project)
        conf1 = project.configurations.create("conf1")
        conf2 = project.configurations.create("conf2")
        task.renderer = renderer
    }

    def "task is configured correctly"() {

  // CHECK: %[[ARG_PERM:.*]] = "tf.Const"() <{value = dense<[0, 2, 3, 1]> : tensor<4xi64>}>
  // CHECK: %[[ARG_TRANSPOSE:[0-9]*]] = "tf.Transpose"(%arg0, %[[ARG_PERM]])

  // CHECK: %[[CONV2D:[0-9]*]] = "tf.Conv2D"(%[[ARG_TRANSPOSE]], %arg1)
  // CHECK-SAME: data_format = "NHWC"
  // CHECK-SAME: dilations = [1, 3, 4, 2]
  // CHECK-SAME: explicit_paddings = [1, 2, 5, 6, 7, 8, 3, 4]
  // CHECK-SAME: padding = "EXPLICIT"

                return true;
            }
            if (moduleConfiguration.equals("*")) {
                boolean include = true;
                for (String conf2 : dependencyConfigurations) {
                    if (conf2.startsWith("!") && conf2.substring(1).equals(configName)) {
                        include = false;
                        break;
                    }
                }

// -----

func.func @conv_with_non_constant_filter(%arg0: tensor<1x3x4x3xf32>, %arg1: tensor<2x3x3x2xf32>) -> tensor<*xf32> {
  %cst = "tf.Const"() {value = dense<0.000000e+00> : tensor<2xf32>} : () -> tensor<2xf32>
  %0 = "tf.Conv2D"(%arg0, %arg1) {data_format = "NHWC", dilations = [1, 1, 2, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true} : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<*xf32>

// CHECK: return %0 : tensor<*xf32>
}

// -----

module {
  func.func @conv2d(%arg0: tensor<1x3x4x512xf32>) -> (tensor<*xf32>) {
    %cst_0 = "tf.Const"() {value = dense<0.000000e+00> : tensor<2xf32>} : () -> tensor<2xf32>

      %outputs_2, %control_3 = tf_executor.island(%control_1) wraps "tf.Const"() {value = dense<0.000000e+00> : tensor<5x5x32x16xf32>} : () -> tensor<5x5x32x16xf32>
      %outputs_4, %control_5 = tf_executor.island wraps "tf.Conv2D"(%outputs, %outputs_2) {data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true} : (tensor<*xf32>, tensor<5x5x32x16xf32>) -> tensor<*xf32>

// CHECK: return %0 : tensor<*xf32>
}

// -----

module {
  func.func @conv2d(%arg0: tensor<1x3x4x3xf32>) -> (tensor<*xf32>) {
    %cst_0 = "tf.Const"() {value = dense<0.000000e+00> : tensor<2xf32>} : () -> tensor<2xf32>

// CHECK-NOT: func private @internal_conv2d_fn
// CHECK-NOT: func private @internal_matmul_fn
// CHECK: func private @quantized_conv2d_with_bias_fn
// CHECK-SAME: tf_quant.quantized_ops = ["Conv2D", "BiasAdd"]
// CHECK: func private @quantized_conv2d_with_bias_and_relu_fn
// CHECK: func private @quantized_conv2d_with_bias_and_relu6_fn
// CHECK: func private @quantized_conv2d_fn

// CHECK-LABEL: conv_2d_bf16
func.func @conv_2d_bf16(%arg0 : tensor<256x32x32x3xbf16>, %arg1 : tensor<3x3x3x16xbf16>) -> tensor<256x8x7x16xbf16> {
  %0 = "tf.Conv2D"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", data_format = "NHWC", dilations = [1, 2, 3, 1], padding = "SAME", strides = [1, 4, 5, 1]} : (tensor<256x32x32x3xbf16>, tensor<3x3x3x16xbf16>) -> tensor<256x8x7x16xbf16>

  // 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>