IntegerAttr dilation_depth_factor, dilation_height_factor,
      dilation_width_factor;
  if (!TFIntListIs1XYZ1(op, "dilations", &dilation_depth_factor,
                        &dilation_height_factor, &dilation_width_factor)) {
    // If the 'dilations' attribute is missing, we use the default value (1)
    // for all dilation depth, height and width factor.
    dilation_depth_factor = rewriter.getI32IntegerAttr(1);

  %cst_2 = "tf.Const"() {value = dense<[0.3, 0.4]> : tensor<2xf32>} : () -> tensor<2xf32>
  %0 = "tf.Conv2D"(%arg_0, %cst_0) {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<1x3x2x2xf32>

  %conv = "tf.Conv2D"(%dq_input, %dq_weight) {attr_map = "0:strides,1:use_cudnn_on_gpu,2:padding,3:explicit_paddings,4:dilations", data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true} : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<*xf32>

    emitError(loc,
              "zero point is expected to be a constant with a single value");
    return {};
  }
  if (strides.size() != num_dims || dilations.size() != num_dims) {
    emitError(loc,
              absl::StrFormat(
                  "strides and dilations are expected to be %d-element arrays",
                  num_dims));
    return {};
  }

  xla::ConvolutionDimensionNumbers dnums;

   // OK
   %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<256x32x32x3xf32>, tensor<3x3x3x16xf32>) -> tensor<256x8x7x16xf32>
   // Unsupported data format

      },
      {
          'testcase_name': 'dilation_bn',
          'activation_fn': None,
          'has_bias': False,
          'has_batch_norm': True,
          'target_opset': quant_opts_pb2.TF,
          'input_shape_dynamic': False,
          'enable_per_channel_quantization': False,
          'dilations': [1, 2, 2, 1],
      },
      {
          'testcase_name': 'with_bias',

  %0 = "tf.Conv2D"(%output, %cst) <{data_format = "NHWC", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 2, 2, 1], use_cudnn_on_gpu = true}> {attr_map = "0:strides,1:use_cudnn_on_gpu,2:padding,3:explicit_paddings,4:dilations", device = ""} : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<1x2x2x2xf32>

  %rst = "tf.Conv2D"(%arg, %fq) {T = "tfdtype$DT_FLOAT", data_format = "NHWC", dilations = [1, 2, 3, 1], padding = "SAME", strides = [1, 4, 5, 1]} : (tensor<256x32x32x3xf32>, tensor<3x3x3x16xf32>) -> tensor<256x8x7x16xf32>
  func.return %rst : tensor<256x8x7x16xf32>

  %conv = "tf.Conv2D"(%dq_input, %dq_weight) {attr_map = "0:strides,1:use_cudnn_on_gpu,2:padding,3:explicit_paddings,4:dilations", data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true} : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<*xf32>

    %0 = "tf.Conv2D"(%arg0, %arg1) {attr_map = "0:strides,1:use_cudnn_on_gpu,2:padding,3:explicit_paddings,4:dilations", data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true} : (tensor<1x2x2x3xf32>, tensor<2x2x3x2xf32>) -> tensor<*xf32>