code: def test_electra_model(compute_layout, ctx):
      with ctx:
          cfg = get_test_cfg()
          cfg.defrost()
          cfg.MODEL.compute_layout = compute_layout
          cfg.freeze()
  
          # Generate TN layout
          cfg_tn = cfg.clone()
          cfg_tn.defrost()
          cfg_tn.MODEL.layout = "TN"
          cfg_tn.freeze()
  
          # Sample data
          batch_size = 4
          sequence_length = 16
          num_mask = 3
          inputs = mx.np.random.randint(
              0, 10, (batch_size, sequence_length)
          )
          token_types = mx.np.random.randint(
              0, 2, (batch_size, sequence_length)
          )
          valid_length = mx.np.random.randint(
              3, sequence_length, (batch_size,)
          )
          masked_positions = mx.np.random.randint(
              0, 3, (batch_size, num_mask)
          )
  
          electra_model = ElectraModel.from_cfg(cfg)
          electra_model.initialize()
          electra_model.hybridize()
          contextual_embedding, pooled_out = electra_model(
              inputs, token_types, valid_length
          )
  
          electra_model_tn = ElectraModel.from_cfg(cfg_tn)
          electra_model_tn.share_parameters(
              electra_model.collect_params()
          )
          electra_model_tn.hybridize()
          contextual_embedding_tn, pooled_out_tn = electra_model_tn(
              inputs.T, token_types.T, valid_length
          )
          assert_allclose(
              contextual_embedding.asnumpy(),
              np.swapaxes(contextual_embedding_tn.asnumpy(), 0, 1),
              1e-4,
              1e-4,
          )
          assert_allclose(
              pooled_out.asnumpy(), pooled_out_tn.asnumpy(), 1e-4, 1e-4
          )
  
          # Verify Float16
          if ctx.device_type == "gpu":
              verify_backbone_fp16(
                  model_cls=ElectraModel,
                  cfg=cfg,
                  ctx=ctx,
                  inputs=[inputs, token_types, valid_length],
              )
  
file_path: gluon-nlp/tests/test_models_electra.py
imports: import pytest
  import numpy
  from numpy.testing import assert_allclose
  import mxnet
  import tempfile
  from gluonnlp.models.electra import ElectraModel, ElectraDiscriminator, ElectraGenerator, list_pretrained_electra, get_pretrained_electra, get_generator_cfg
  from gluonnlp.utils.testing import verify_backbone_fp16
label_desc: Use image node_id 3 for calling a global function with example usage: test_electra_model(compute_layout, ctx) without return types