docstr: Returns a list of all possible combinations of the parameters in the config classes.
  
  Args:
      grid_parameters (`dict`):
          A dictionary containing the parameters to be tested. There should be at least the key "model_ids" which
          contains a list of model ids to be tested. The other keys should be the name of the config class
          post-fixed with "_kwargs" and the value should be a dictionary containing the parameters to be tested
          for that config class.
      filter_params_func (`callable`, `optional`):
          A function that takes a list of tuples and returns a list of tuples. This function is used to filter
          out the tests that needs for example to be skipped.
  
  Returns:
      generated_tests (`list`):
          A list of tuples containing the name of the test, the model id, the config class and the config class
          kwargs.
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code: def get_grid_parameters(
      self, grid_parameters, filter_params_func=None
  ):
      r"""
      Returns a list of all possible combinations of the parameters in the config classes.
  
      Args:
          grid_parameters (`dict`):
              A dictionary containing the parameters to be tested. There should be at least the key "model_ids" which
              contains a list of model ids to be tested. The other keys should be the name of the config class
              post-fixed with "_kwargs" and the value should be a dictionary containing the parameters to be tested
              for that config class.
          filter_params_func (`callable`, `optional`):
              A function that takes a list of tuples and returns a list of tuples. This function is used to filter
              out the tests that needs for example to be skipped.
  
      Returns:
          generated_tests (`list`):
              A list of tuples containing the name of the test, the model id, the config class and the config class
              kwargs.
      """
      generated_tests = []
      model_list = grid_parameters["model_ids"]
      task_type = (
          grid_parameters["task_type"]
          if "task_type" in grid_parameters
          else None
      )
  
      for model_id in model_list:
          for key, value in self.items():
              if "{}_kwargs".format(key) in grid_parameters:
                  peft_configs = []
                  current_peft_config = value[1].copy()
                  for current_key, current_value in grid_parameters[
                      f"{key}_kwargs"
                  ].items():
                      for kwarg in current_value:
                          current_peft_config.update(
                              {current_key: kwarg}
                          )
  
                          if task_type is not None:
                              current_peft_config.update(
                                  {"task_type": task_type}
                              )
  
                          peft_configs.append(
                              current_peft_config.copy()
                          )
              else:
                  current_peft_config = value[1].copy()
                  if task_type is not None:
                      current_peft_config.update(
                          {"task_type": task_type}
                      )
                  peft_configs = [current_peft_config]
  
              for peft_config in peft_configs:
                  generated_tests.append(
                      (
                          f"test_{model_id}_{key}",
                          model_id,
                          value[0],
                          peft_config,
                      )
                  )
  
      if filter_params_func is not None:
          generated_tests = filter_params_func(generated_tests)
  
      return generated_tests
  
imports: import copy
  import json
  import os
  import pickle
  import re
  import tempfile
  from collections import OrderedDict
  from dataclasses import replace
  import torch
  import yaml
  from diffusers import StableDiffusionPipeline
  from peft import AdaLoraConfig, IA3Config, LoraConfig, PeftModel, PeftType, PrefixTuningConfig, PromptEncoderConfig, PromptLearningConfig, PromptTuningConfig, get_peft_model, get_peft_model_state_dict, prepare_model_for_int8_training
  from peft.tuners.lora import LoraLayer
  from peft.utils import _get_submodules, infer_device
  from .testing_utils import get_state_dict
label_desc: Use image node_id 2 for calling the ClassInstantier obj's underlying member method code with example usage: obj.get_grid_parameters(grid_parameters, filter_params_func) and returns: generated_tests