code: def predict(self, df, args=None):
      fnc_list = {
          "text-classification": self.predict_text_classification,
          "zero-shot-classification": self.predict_zero_shot,
          "translation": self.predict_translation,
          "summarization": self.predict_summarization,
          "fill-mask": self.predict_fill_mask,
      }
  
      ###### get stuff from model folder
      args = self.model_storage.json_get("args")
  
      task = args["task"]
  
      if task not in fnc_list:
          raise RuntimeError(f"Unknown task: {task}")
  
      fnc = fnc_list[task]
  
      try:
          # load from model storage (finetuned models will use this)
          hf_model_storage_path = self.model_storage.folder_get(
              args["model_name"]
          )
          pipeline = transformers.pipeline(
              task=args["task_proper"],
              model=hf_model_storage_path,
              tokenizer=hf_model_storage_path,
          )
      except OSError:
          # load from engine storage (i.e. 'common' models)
          hf_model_storage_path = self.engine_storage.folder_get(
              args["model_name"]
          )
          pipeline = transformers.pipeline(
              task=args["task_proper"],
              model=hf_model_storage_path,
              tokenizer=hf_model_storage_path,
          )
  
      input_column = args["input_column"]
      if input_column not in df.columns:
          raise RuntimeError(
              f'Column "{input_column}" not found in input data'
          )
      input_list = df[input_column]
  
      max_tokens = pipeline.tokenizer.model_max_length
  
      results = []
      for item in input_list:
          if max_tokens is not None:
              tokens = pipeline.tokenizer.encode(item)
              if len(tokens) > max_tokens:
                  truncation_policy = args.get(
                      "truncation_policy", "strict"
                  )
                  if truncation_policy == "strict":
                      results.append(
                          {
                              "error": f"Tokens count exceed model limit: {len(tokens)} > {max_tokens}"
                          }
                      )
                      continue
                  elif truncation_policy == "left":
                      tokens = tokens[
                          -max_tokens + 1 : -1
                      ]  # cut 2 empty tokens from left and right
                  else:
                      tokens = tokens[
                          1 : max_tokens - 1
                      ]  # cut 2 empty tokens from left and right
  
                  item = pipeline.tokenizer.decode(tokens)
  
          item = str(item)
          try:
              result = fnc(pipeline, item, args)
          except Exception as e:
              msg = str(e).strip()
              if msg == "":
                  msg = e.__class__.__name__
              result = {"error": msg}
          results.append(result)
  
      pred_df = pd.DataFrame(results)
  
      return pred_df
  
file_path: mindsdb/mindsdb/integrations/handlers/huggingface_handler/huggingface_handler.py
imports: from typing import Dict, Optional
  import pandas
  import transformers
  from huggingface_hub import HfApi
  from mindsdb.integrations.handlers.huggingface_handler.settings import FINETUNE_MAP
  from mindsdb.integrations.libs.base import BaseMLEngine
  from mindsdb.utilities import log
label_desc: Use image node_id 9 for calling the HuggingFaceHandler obj's underlying member method code with example usage: obj.predict(df, args) and returns: pred_df