code: def __call__(self, output_text, **kwargs):
      from transformers import (
          AutoModelForSeq2SeqLM,
          AutoTokenizer,
          AutoConfig,
      )
  
      config = AutoConfig.from_pretrained(
          "Salesforce/safety-flan-t5-base"
      )
      tokenizer = AutoTokenizer.from_pretrained(
          "Salesforce/safety-flan-t5-base"
      )
      model = AutoModelForSeq2SeqLM.from_pretrained(
          "Salesforce/safety-flan-t5-base", config=config
      )
      model.eval()
  
      prefix = "Is the <Text> field safe or unsafe?"
      input_ids = tokenizer(
          prefix + " <Text> " + output_text + " <Context> ",
          return_tensors="pt",
      ).input_ids
  
      if len(input_ids[0]) > 512:
          warnings.warn(
              "Input length is > 512 token. Safety check result could be incorrect."
          )
  
      with torch.no_grad():
          outputs = model.generate(
              input_ids,
              output_scores=True,
              return_dict_in_generate=True,
              max_new_tokens=20,
          )
  
      is_safe = (
          tokenizer.decode(
              outputs.sequences[0], skip_special_tokens=True
          ).split(" ")[0]
          == "safe"
      )
  
      report = ""
      if not is_safe:
          true_false_ids = tokenizer("true false").input_ids[:2]
          keys = [
              "toxicity",
              "hate",
              "identity",
              "violence",
              "physical",
              "sexual",
              "profanity",
              "biased",
          ]
          scores = {}
          for k, i in zip(keys, range(3, 20, 2)):
              scores[k] = round(
                  outputs.scores[i][0, true_false_ids]
                  .softmax(dim=0)[0]
                  .item(),
                  5,
              )
  
          report += (
              "|" + "|".join(f"{n:^10}" for n in scores.keys()) + "|\n"
          )
          report += (
              "|"
              + "|".join(f"{n:^10}" for n in scores.values())
              + "|\n"
          )
      return "Salesforce Content Safety Flan T5 Base", is_safe, report
  
imports: import os
  import torch
  import warnings
  from typing import List
  from string import Template
  from enum import Enum
label_desc: Use image node_id 2 for calling the SalesforceSafetyChecker obj's underlying member method code with example usage: obj.__call__(output_text) and returns: str,  is_safe,  report