code: def get_policy(model_name):
      if model_name == "opt-6.7b":
          gpu_batch_size = 1
          num_gpu_batches = 1
          percent = (100, 0, 90, 10, 100, 0)
          cpu_cache_compute = False
      elif model_name == "opt-30b":
          gpu_batch_size = 5
          num_gpu_batches = 3
          percent = (20, 80, 0, 100, 0, 100)
          cpu_cache_compute = True
      else:
          raise Exception("unsupported model")
  
      policy = Policy(
          gpu_batch_size,
          num_gpu_batches,
          percent[0],
          percent[1],
          percent[2],
          percent[3],
          percent[4],
          percent[5],
          overlap=True,
          sep_layer=True,
          pin_weight=True,
          cpu_cache_compute=cpu_cache_compute,
          attn_sparsity=1.0,
          compress_weight=False,
          comp_weight_config=CompressionConfig(
              num_bits=4, group_size=64, group_dim=0, symmetric=False
          ),
          compress_cache=False,
          comp_cache_config=CompressionConfig(
              num_bits=4, group_size=64, group_dim=2, symmetric=False
          ),
      )
      return policy
  
file_path: H2O/h2o_flexgen/benchmark/xsum/run_xsum_flexgen.py
imports: import argparse
  import numpy
  import torch
  import json
  import time
  from tqdm import tqdm
  import copy
  from flexgen.flex_opt import Policy, OptLM, ExecutionEnv, CompressionConfig
  from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig
label_desc: Use image node_id 3 for calling a global function with example usage: get_policy(model_name) and returns: policy