code: def test_rnn_unroll_variant_length():
      # Test for imperative usage
      cell_list = []
      for base_cell_class in [
          gluon.rnn.RNNCell,
          gluon.rnn.LSTMCell,
          gluon.rnn.GRUCell,
      ]:
          cell_list.append(base_cell_class(20))
          cell_list.append(
              gluon.rnn.BidirectionalCell(
                  l_cell=base_cell_class(20), r_cell=base_cell_class(20)
              )
          )
          cell_list.append(
              gluon.rnn.VariationalDropoutCell(
                  base_cell=base_cell_class(20)
              )
          )
      stack_res_rnn_cell = gluon.rnn.SequentialRNNCell()
      stack_res_rnn_cell.add(
          gluon.rnn.ResidualCell(base_cell=gluon.rnn.RNNCell(20))
      )
      stack_res_rnn_cell.add(
          gluon.rnn.ResidualCell(base_cell=gluon.rnn.RNNCell(20))
      )
      cell_list.append(stack_res_rnn_cell)
      batch_size = 4
      max_length = 10
      valid_length = [3, 10, 5, 6]
      valid_length_nd = mx.np.array(valid_length)
      for cell in cell_list:
          # Test for NTC layout
          print(type(cell))
          data_nd = mx.np.random.normal(
              0, 1, size=(batch_size, max_length, 20)
          )
          if isinstance(
              cell,
              (
                  gluon.rnn.HybridSequentialRNNCell,
                  gluon.rnn.SequentialRNNCell,
              ),
          ):
              cell.infer_shape(0, data_nd, False)
          elif isinstance(cell, gluon.rnn.BidirectionalCell):
              cell.infer_shape(0, data_nd, True)
          else:
              cell.infer_shape(0, data_nd, False)
          cell.initialize()
          cell.hybridize()
          outs, states = cell.unroll(
              length=max_length,
              inputs=data_nd,
              valid_length=valid_length_nd,
              merge_outputs=True,
              layout="NTC",
          )
          for i, ele_length in enumerate(valid_length):
              # Explicitly unroll each sequence and compare the final states and output
              ele_out, ele_states = cell.unroll(
                  length=ele_length,
                  inputs=data_nd[i : (i + 1), :ele_length, :],
                  merge_outputs=True,
                  layout="NTC",
              )
              assert_almost_equal(
                  ele_out, outs[i : (i + 1), :ele_length, :]
              )
              if ele_length < max_length:
                  # Check the padded outputs are all zero
                  assert_almost_equal(
                      outs[i : (i + 1), ele_length:max_length, :], 0
                  )
              for valid_out_state, gt_state in zip(states, ele_states):
                  assert_almost_equal(
                      valid_out_state[i : (i + 1)], gt_state
                  )
  
          # Test for TNC layout
          data_nd = mx.np.random.normal(
              0, 1, size=(max_length, batch_size, 20)
          )
          outs, states = cell.unroll(
              length=max_length,
              inputs=data_nd,
              valid_length=valid_length_nd,
              layout="TNC",
          )
          for i, ele_length in enumerate(valid_length):
              # Explicitly unroll each sequence and compare the final states and output
              ele_out, ele_states = cell.unroll(
                  length=ele_length,
                  inputs=data_nd[:ele_length, i : (i + 1), :],
                  merge_outputs=True,
                  layout="TNC",
              )
              assert_almost_equal(
                  ele_out, outs[:ele_length, i : (i + 1), :]
              )
              if ele_length < max_length:
                  # Check the padded outputs are all zero
                  assert_almost_equal(
                      outs[ele_length:max_length, i : (i + 1), :], 0
                  )
              for valid_out_state, gt_state in zip(states, ele_states):
                  assert_almost_equal(
                      valid_out_state[i : (i + 1)], gt_state
                  )
  
imports: import mxnet
  from mxnet import gluon, np
  import numpy
  import copy
  from functools import partial
  from numpy.testing import assert_allclose
  from mxnet.gluon.utils import split_rnn_params
  import pytest
  from mxnet.test_utils import almost_equal, assert_almost_equal, default_device
  from common import assert_raises_cudnn_not_satisfied, retry
label_desc: Use image node_id 28 for calling a global function with example usage: test_rnn_unroll_variant_length() without return types