code: def test_single_sentence_albert():
      MODEL_LIST = [
          transformers.AlbertModel,
          transformers.AlbertForPreTraining,
          transformers.AlbertForMaskedLM,
          transformers.AlbertForSequenceClassification,
          transformers.AlbertForTokenClassification,
      ]
  
      config = transformers.AlbertConfig(
          vocab_size=100,
          embedding_size=128,
          hidden_size=128,
          num_hidden_layers=2,
          num_attention_heads=4,
          intermediate_size=256,
      )
  
      def data_gen():
          input_ids = torch.zeros(
              (BATCH_SIZE, SEQ_LENGHT), dtype=torch.int64
          )
          token_type_ids = torch.zeros(
              (BATCH_SIZE, SEQ_LENGHT), dtype=torch.int64
          )
          attention_mask = torch.zeros(
              (BATCH_SIZE, SEQ_LENGHT), dtype=torch.int64
          )
          meta_args = dict(
              input_ids=input_ids,
              token_type_ids=token_type_ids,
              attention_mask=attention_mask,
          )
          return meta_args
  
      for model_cls in MODEL_LIST:
          model = model_cls(config=config)
          split_model_and_compare_output(model, data_gen)
  
imports: import pytest
  import torch
  import transformers
  from hf_utils import split_model_and_compare_output
label_desc: Use image node_id 1 for calling a global function with example usage: test_single_sentence_albert() and returns: meta_args