} else {
      output = rewriter.create<TF::Conv2DOp>(
          conv_op.getLoc(), conv_output_type, sliced_lhs, rhs,
          rewriter.getI64ArrayAttr(strides),
          /*use_cudnn_on_gpu=*/rewriter.getBoolAttr(true),
          /*padding=*/rewriter.getStringAttr(padding),
          /*explicit_paddings=*/rewriter.getI64ArrayAttr(new_padding),
          /*data_format=*/rewriter.getStringAttr("NHWC"),

    %1 = "tf.Conv2DBackpropInput"(%0, %arg0, %arg1) {data_format = "NHWC", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true} : (tensor<4xi32>, tensor<3x3x32x64xf32>, tensor<200x24x24x64xf32>) -> tensor<?x?x?x?xf32>
    func.return %1 : tensor<?x?x?x?xf32>
  }

  // CHECK-LABEL: func @shape_from_if_to_branch_functions_to_results