code: def test_numerical_equivalence_single_batch_jax_scan(self):
      batch_size = 3
      seq_len = 4
      channels_in = 5
      channels_out = 6
  
      xs = jax.random.uniform(
          jax.random.key(0), (batch_size, seq_len, channels_in)
      )
      cell: nn.LSTMCell = nn.LSTMCell(channels_out)
      carry = cell.initialize_carry(jax.random.key(0), xs[:, 0].shape)
      variables = cell.init(jax.random.key(0), carry, xs[:, 0])
      cell_params = variables["params"]
  
      def scan_fn(carry, x):
          return cell.apply({"params": cell_params}, carry, x)
  
      ys: jnp.ndarray
      carry, ys = jax.lax.scan(scan_fn, carry, xs.swapaxes(0, 1))
      ys = ys.swapaxes(0, 1)
  
      cell_carry = cell.initialize_carry(
          jax.random.key(0), xs[:, 0].shape
      )
  
      for i in range(seq_len):
          cell_carry, y = cell.apply(
              {"params": cell_params}, cell_carry, xs[:, i, :]
          )
          np.testing.assert_allclose(y, ys[:, i, :], rtol=1e-4)
  
      np.testing.assert_allclose(cell_carry, carry, rtol=1e-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 10 for calling the RNNTest obj's underlying member method code with example usage: obj.test_numerical_equivalence_single_batch_jax_scan() and returns: cell