code: def _get_model(
      self,
      model_cls,
      peft_config=None,
      adapter_name=None,
      seed=0,
      mixed=True,
  ):
      torch.manual_seed(
          0
      )  # always use seed 0 for base model, seed for adapters may differ
      base_model = model_cls().eval().to(self.torch_device)
      if peft_config is None:
          return base_model
  
      torch.manual_seed(seed)
      assert adapter_name is not None
      peft_model = get_peft_model(
          base_model,
          peft_config,
          adapter_name=adapter_name,
          mixed=mixed,
      )
      return peft_model.eval().to(self.torch_device)
  
imports: import copy
  import itertools
  import os
  import re
  import tempfile
  import unittest
  import torch
  from parameterized import parameterized
  from torch import nn
  from transformers import AutoModelForCausalLM
  from peft import AdaLoraConfig, LoHaConfig, LoKrConfig, LoraConfig, OFTConfig, PeftMixedModel, PrefixTuningConfig, get_peft_model
  from peft.tuners.tuners_utils import BaseTunerLayer
  from peft.utils import infer_device
label_desc: Use image node_id 1 for calling the TestMixedAdapterTypes obj's underlying member method code with example usage: obj._get_model(model_cls, peft_config, adapter_name, seed, mixed) and returns: peft_model, base_model