code: def sample(self, texts, *args, **kwargs):
      if "max_tokens" in kwargs:
          kwargs["max_new_tokens"] = kwargs["max_tokens"]
          kwargs.pop("max_tokens")
      generated = self.generate(
          texts,
          do_sample=False,
          num_beams=1,
          output_scores=True,
          return_dict_in_generate=True,
          *args,
          **kwargs,
      )
      decoded = self.tokenizer.batch_decode(
          generated.sequences, skip_special_tokens=True
      )
      target_completions = [
          d.replace(t, "") for d, t in zip(decoded, texts)
      ]
      return target_completions
  
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 7 for calling the HF_LM obj's underlying member method code with example usage: obj.sample(texts) and returns: target_completions