code: def generate(self, texts, *args, **kwargs):
      tokenized = self.tokenizer(
          texts, return_tensors="pt", padding=True
      ).to(self.device)
      generated = self.model.generate(
          input_ids=tokenized["input_ids"],
          attention_mask=tokenized["attention_mask"],
          pad_token_id=self.tokenizer.pad_token_id,
          *args,
          **kwargs,
      )
      torch.cuda.empty_cache()
      return generated
  
file_path: interpret-text/python/interpret_text/generative/model_lib/hf_tooling.py
imports: import torch
  import numpy
  from tqdm import tqdm
  import torch.nn.functional
  from scipy.special import log_softmax, logsumexp
label_desc: Use image node_id 5 for calling the HF_LM obj's underlying member method code with example usage: obj.generate(texts) and returns: generated