code: def test_lstm_clip():
      hidden_size, projection_size = 4096, 2048
      batch_size, seq_len = 32, 80
      input_size = 50
      clip_min, clip_max, clip_nan = -5, 5, True
      lstm_input = mx.np.random.uniform(
          size=(seq_len, batch_size, input_size), device=mx.gpu(0)
      )
      lstm_states = [
          mx.np.random.uniform(
              size=(2, batch_size, projection_size), device=mx.gpu(0)
          ),
          mx.np.random.uniform(
              size=(2, batch_size, hidden_size), device=mx.gpu(0)
          ),
      ]
      lstm_layer = gluon.rnn.LSTM(
          hidden_size,
          projection_size=projection_size,
          input_size=input_size,
          bidirectional=True,
          state_clip_min=clip_min,
          state_clip_max=clip_max,
          state_clip_nan=clip_nan,
      )
      lstm_layer.initialize(device=mx.gpu(0))
      with autograd.record():
          _, layer_output_states = lstm_layer(lstm_input, lstm_states)
      cell_states = layer_output_states[0]
      assert (cell_states >= clip_min).all() and (
          cell_states <= clip_max
      ).all()
      assert not _np.isnan(cell_states).any()
  
imports: import sys
  import os
  import time
  import random
  import mxnet
  import multiprocessing
  from mxnet.test_utils import check_consistency, set_default_device, assert_almost_equal, rand_ndarray, environment
  import numpy
  import math
  from mxnet import autograd
  import pytest
  from common import assert_raises_cudnn_not_satisfied, run_in_spawned_process, random_seed
  from test_gluon import *
  from test_loss import *
  from test_numpy_loss import *
  from test_gluon_rnn import *
label_desc: Use image node_id 4 for calling a global function with example usage: test_lstm_clip() without return types