return ...
      }
      func @_func(%input: tensor<2x224x224x3xf32>, %filter: tensor<7x7x3x64xf32>) {
        %6 = "tf.Conv2D"(%input, %filter)  {strides = [1, 2, 2, 1]}: (tensor<2x230x230x3xf32>, tensor<7x7x3x64xf32>) -> tensor<2x112x112x64xf32>
      }
    }
    ```

    The program will be transformed into:

    ```mlir
    module {

  %129 = "tf.Transpose"(%127, %cst_14) : (tensor<6x3x7x7xf32>, tensor<4xi64>) -> tensor<7x7x3x6xf32>
  %130 = "tf.Conv2D"(%arg0, %129) <{data_format = "NHWC", dilations = [1, 1, 1, 1], explicit_paddings = [0, 0, 3, 3, 3, 3, 0, 0], padding = "EXPLICIT", strides = [1, 2, 2, 1], use_cudnn_on_gpu = true}> : (tensor<1x224x224x3xf32>, tensor<7x7x3x6xf32>) -> tensor<1x112x112x6xf32>
  return %130 : tensor<1x112x112x6xf32>

// CHECK:           %[[VAL_2:.*]] = "tf.Conv2D"(%[[VAL_0]], %[[VAL_1]]) <{data_format = "NHWC", dilations = [1, 1, 1, 1], explicit_paddings = [0, 0, 1, 1, 1, 1, 0, 0], padding = "EXPLICIT", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true}> : (tensor<?x8x8x8xf32>, tensor<8x8x8x64xf32>) -> tensor<?x3x3x64xf32>
// CHECK:           return %[[VAL_2]] : tensor<?x3x3x64xf32>
// CHECK:         }