code: def test_numerical_equivalence_with_mask(self):
      batch_size = 3
      seq_len = 4
      channels_in = 5
      channels_out = 6
  
      key = jax.random.key(0)
      seq_lengths = jax.random.randint(
          key, (batch_size,), minval=1, maxval=seq_len + 1
      )
  
      rnn = nn.RNN(nn.LSTMCell(channels_out), return_carry=True)
  
      xs = jnp.ones((batch_size, seq_len, channels_in))
      ys: jnp.ndarray
      (carry, ys), variables = rnn.init_with_output(
          jax.random.key(0), xs, seq_lengths=seq_lengths
      )
  
      cell_carry = rnn.cell.initialize_carry(
          jax.random.key(0), xs[:, 0].shape
      )
      cell_params = variables["params"]["cell"]
      carries = []
  
      for i in range(seq_len):
          cell_carry, y = rnn.cell.apply(
              {"params": cell_params}, cell_carry, xs[:, i, :]
          )
          np.testing.assert_allclose(y, ys[:, i, :], rtol=1e-5)
          carries.append(cell_carry)
  
      for batch_idx, length in enumerate(seq_lengths):
          t = int(length) - 1
          for carries_t_, carry_ in zip(carries[t], carry):
              np.testing.assert_allclose(
                  carries_t_[batch_idx], carry_[batch_idx], rtol=1e-5
              )
  
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 7 for calling the RNNTest obj's underlying member method code with example usage: obj.test_numerical_equivalence_with_mask() without return types