code: def test_rnn_with_spatial_dimensions(self):
      batch_size = 10
      seq_len = 40
      kernel_size = (3, 3)
      image_size = (32, 32)
      channels_in = 5
      channels_out = 15
  
      rnn = nn.RNN(
          nn.ConvLSTMCell(channels_out, kernel_size),
      )
  
      xs = jnp.ones((batch_size, seq_len, *image_size, channels_in))
      variables = rnn.init(jax.random.key(0), xs)
  
      ys: jnp.ndarray
      carry, ys = rnn.apply(variables, xs, return_carry=True)
  
      # carry state should not be zeros after apply
      for leaf in jax.tree_util.tree_leaves(carry):
          assert not np.allclose(leaf, jnp.zeros_like(leaf))
          self.assertEqual(leaf.shape[:-1], (batch_size, *image_size))
          self.assertIn(leaf.shape[-1], [channels_in, channels_out])
  
      self.assertEqual(
          ys.shape, (batch_size, seq_len, *image_size, channels_out)
      )
  
      for layer_params in variables["params"]["cell"].values():
          if "bias" in layer_params:
              self.assertEqual(
                  layer_params["bias"].shape, (channels_out * 4,)
              )
          self.assertIn(
              layer_params["kernel"].shape[2],
              [channels_in, channels_out, channels_out * 4],
          )
          self.assertEqual(
              layer_params["kernel"].shape[3], channels_out * 4
          )
  
imports: import jax
  import jax.numpy
  import numpy
  from absl.testing import absltest
  from flax import linen
  from flax.linen.recurrent import flip_sequences
label_desc: Use image node_id 5 for calling the RNNTest obj's underlying member method code with example usage: obj.test_rnn_with_spatial_dimensions() without return types