code: def test_gpt2_incremental_states(ctx):
      with ctx:
          batch_size = 4
          sequence_length = 5
          inputs = mx.np.random.randint(
              0, 1000, (batch_size, sequence_length), ctx=ctx
          )
  
          cfg = GPT2Model.get_cfg()
          gpt2_model = GPT2Model.from_cfg(cfg)
          gpt2_model.initialize(ctx=ctx)
          gpt2_model.hybridize()
  
          one_time_hiddens, one_time_states = gpt2_model(
              inputs, gpt2_model.init_states(batch_size, ctx)
          )
  
          states = gpt2_model.init_states(batch_size, ctx)
          hiddens_l = []
          for i in range(sequence_length):
              hiddens, states = gpt2_model(inputs[:, i : i + 1], states)
              hiddens_l.append(hiddens)
          hiddens_concat = mx.np.concatenate(hiddens_l, axis=1)
          assert_allclose(
              one_time_states.asnumpy(), states.asnumpy(), 1e-4, 1e-4
          )
          assert_allclose(
              one_time_hiddens.asnumpy(),
              hiddens_concat.asnumpy(),
              1e-4,
              1e-4,
          )
  
file_path: gluon-nlp/tests/test_models_gpt2.py
imports: import pytest
  import numpy
  import mxnet
  import tempfile
  from numpy.testing import assert_allclose
  from gluonnlp.models.gpt2 import GPT2Model, GPT2ForLM, list_pretrained_gpt2, get_pretrained_gpt2
  from gluonnlp.loss import LabelSmoothCrossEntropyLoss
  from gluonnlp.utils.testing import verify_backbone_fp16
label_desc: Use image node_id 3 for calling a global function with example usage: test_gpt2_incremental_states(ctx) without return types