code: def set_generic_model_params(discrim_weights, discrim_meta):
      if discrim_weights is None:
          raise ValueError(
              "When using a generic discriminator, discrim_weights need to be specified"
          )
      if discrim_meta is None:
          raise ValueError(
              "When using a generic discriminator, discrim_meta need to be specified"
          )
  
      with open(discrim_meta, "r") as discrim_meta_file:
          meta = json.load(discrim_meta_file)
      meta["path"] = discrim_weights
      DISCRIMINATOR_MODELS_PARAMS["generic"] = meta
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/examples/research_projects/pplm/run_pplm.py
imports: import argparse
  import json
  from operator import add
  from typing import List, Optional, Tuple, Union
  import numpy
  import torch
  from torch import nn
  from tqdm import trange
  from pplm_classification_head import ClassificationHead
  from transformers import GPT2LMHeadModel, GPT2Tokenizer
  from transformers.file_utils import cached_path
label_desc: Use image node_id 8 for calling a global function with example usage: set_generic_model_params(discrim_weights, discrim_meta) without return types