code: def create_model(model_type: str):
      pruning_torch_config_list = [
          {
              "op_types": ["Linear"],
              "exclude_op_names": ["fc2"],
              "sparse_ratio": 0.75,
              "granularity": "out_channel",
          },
          {
              "op_names": ["conv1", "conv2", "conv3"],
              "sparse_ratio": 0.75,
              "granularity": "out_channel",
          },
      ]
  
      distil_torch_config_list = [
          {"op_types": ["Linear"], "lambda": 0.75},
          {"op_names": ["conv1", "conv2", "conv3"], "lambda": 0.75},
      ]
  
      pruning_lightning_config_list = [
          {
              "op_types": ["Linear"],
              "exclude_op_names": ["model.fc2"],
              "sparse_ratio": 0.75,
              "granularity": "out_channel",
          },
          {
              "op_names": ["model.conv1", "model.conv2", "model.conv3"],
              "sparse_ratio": 0.75,
              "granularity": "out_channel",
          },
      ]
  
      distil_lightning_config_list = [
          {"op_types": ["Linear"], "lambda": 0.75},
          {
              "op_names": ["model.conv1", "model.conv2", "model.conv3"],
              "lambda": 0.75,
          },
      ]
  
      quant_config_list = [
          {"op_names": ["fc1", "fc2"], "quant_dtype": "int8"},
          {
              "op_names": ["conv1", "conv2", "conv3"],
              "quant_dtype": "int8",
              "fuse_names": [
                  ("conv1", "bn1"),
                  ("conv2", "bn2"),
                  ("conv3", "bn3"),
              ],
          },
      ]
  
      quant_lightning_config_list = [
          {
              "op_names": ["model.fc1", "model.fc2"],
              "quant_dtype": "int8",
          },
          {
              "op_names": ["model.conv1", "model.conv2", "model.conv3"],
              "quant_dtype": "int8",
              "fuse_names": [
                  ("model.conv1", "model.bn1"),
                  ("model.conv2", "model.bn2"),
                  ("model.conv3", "model.bn3"),
              ],
          },
      ]
  
      if model_type == "lightning":
          model = SimpleLightningModel()
          config_list_dict = {
              "pruning": pruning_lightning_config_list,
              "distillation": distil_lightning_config_list,
              "quantization": quant_lightning_config_list,
          }
          dummy_input = torch.rand(8, 1, 28, 28)
      elif model_type == "pytorch":
          model = SimpleTorchModel().to(device)
          config_list_dict = {
              "pruning": pruning_torch_config_list,
              "distillation": distil_torch_config_list,
              "quantization": quant_config_list,
          }
          dummy_input = torch.rand(8, 1, 28, 28, device=device)
      else:
          raise ValueError(f"wrong model_type: {model_type}")
      return model, config_list_dict, dummy_input
  
imports: from pathlib import Path
  import torch
  from .device import device
  from .simple_mnist import SimpleLightningModel, SimpleTorchModel
label_desc: Use image node_id 1 for calling a global function with example usage: create_model(model_type) and returns: model,  config_list_dict,  dummy_input