code: def test_conv_kernel():
      """Test convolution kernel with various input formats."""
      filter_size_1 = (
          4  # The size of squared filter for the first layer
      )
      filter_size_2 = (
          3,
          5,
      )  # The size of non-squared filter for the second layer
      target_shape_1 = [
          2,
          52,
          40,
          32,
      ]  # The desired shape of the first layer
      target_shape_2 = [
          2,
          13,
          9,
          32,
      ]  # The desired shape of the second layer
      kwargs = {}
      n_envs = 1
      n_steps = 2
      n_batch = n_envs * n_steps
      scale = False
      env = gym.make(ENV_ID)
      ob_space = env.observation_space
  
      with tf.Graph().as_default():
          _, scaled_images = observation_input(
              ob_space, n_batch, scale=scale
          )
          activ = tf.nn.relu
          layer_1 = activ(
              conv(
                  scaled_images,
                  "c1",
                  n_filters=32,
                  filter_size=filter_size_1,
                  stride=4,
                  init_scale=np.sqrt(2),
                  **kwargs,
              )
          )
          layer_2 = activ(
              conv(
                  layer_1,
                  "c2",
                  n_filters=32,
                  filter_size=filter_size_2,
                  stride=4,
                  init_scale=np.sqrt(2),
                  **kwargs,
              )
          )
          assert layer_1.shape == target_shape_1, (
              "The shape of layer based on the squared kernel matrix is not correct. "
              "The current shape is {} and the desired shape is {}".format(
                  layer_1.shape, target_shape_1
              )
          )
          assert layer_2.shape == target_shape_2, (
              "The shape of layer based on the non-squared kernel matrix is not correct. "
              "The current shape is {} and the desired shape is {}".format(
                  layer_2.shape, target_shape_2
              )
          )
      env.close()
  
imports: import gym
  import numpy
  import tensorflow
  from stable_baselines.common.tf_layers import conv
  from stable_baselines.common.input import observation_input
label_desc: Use image node_id 1 for calling a global function with example usage: test_conv_kernel() without return types