class_docstr: Create a Unet-based generator
class_docstr_tok: ["Create","a","Unet-based","generator"]
code: def _fetch_arguments_up_and_down(self, opt, index):
      # Defines down arguments
      args_down = self._fetch_arguments_from_list(opt.down_conv, index)
      args_down["index"] = index
      args_down["down_conv_cls"] = self._factory_module.get_module(
          "DOWN"
      )
  
      # Defines up arguments
      idx = len(getattr(opt.up_conv, "up_conv_nn")) - index - 1
      args_up = self._fetch_arguments_from_list(opt.up_conv, idx)
      args_up["index"] = index
      args_up["up_conv_cls"] = self._factory_module.get_module("UP")
      return args_up, args_down
  
imports: from torch import nn
  from torch_geometric.nn import global_max_pool, global_mean_pool, fps, radius, knn_interpolate
  from torch.nn import Linear, ReLU, LeakyReLU, BatchNorm1d, Dropout
  from omegaconf.listconfig import ListConfig
  from omegaconf.dictconfig import DictConfig
  import logging
  import copy
  from torch_points3d.datasets.base_dataset import BaseDataset
  from torch_points3d.models.base_model import BaseModel
  from torch_points3d.core.common_modules.base_modules import Identity
  from torch_points3d.utils.config import is_list
label_desc: Use image node_id 7 for calling the UnetBasedModel obj's underlying member method code with example usage: obj._fetch_arguments_up_and_down(opt, index) and returns: args_up,  args_down