code: def _initialize(self, use_gpu=False):
      # Load Paddlehub ERNIE Tiny pretrained model
      self.module = hub.Module(name="ernie_tiny")
      inputs, outputs, program = self.module.context(
          trainable=True, max_seq_len=128
      )
  
      # Download dataset and get its label list and label num
      # If you just want labels information, you can omit its tokenizer parameter to avoid preprocessing the train set.
      dataset = hub.dataset.Couplet()
      self.label_list = dataset.get_labels()
  
      # Setup RunConfig for PaddleHub Fine-tune API
      config = hub.RunConfig(
          use_data_parallel=False,
          use_cuda=use_gpu,
          batch_size=1,
          checkpoint_dir=os.path.join(self.directory, "assets", "ckpt"),
          strategy=hub.AdamWeightDecayStrategy(),
      )
  
      # Construct transfer learning network
      # Use "pooled_output" for classification tasks on an entire sentence.
      # Use "sequence_output" for token-level output.
      pooled_output = outputs["pooled_output"]
      sequence_output = outputs["sequence_output"]
  
      # Define a classfication fine-tune task by PaddleHub's API
      self.gen_task = hub.TextGenerationTask(
          feature=pooled_output,
          token_feature=sequence_output,
          max_seq_len=128,
          num_classes=dataset.num_labels,
          config=config,
          metrics_choices=["bleu"],
      )
  
file_path: PaddleHub/modules/text/text_generation/ernie_tiny_couplet/module.py
imports: import os
  import ast
  import argparse
  import paddlehub
  from paddlehub.module.module import moduleinfo, serving, runnable
  from paddlehub.module.nlp_module import DataFormatError
label_desc: Use image node_id 1 for calling the ErnieTinyCouplet obj's underlying member method code with example usage: obj._initialize(use_gpu) without return types