class_docstr: Create a Unet unwrapped generator
class_docstr_tok: ["Create","a","Unet","unwrapped","generator"]
docstr: Fetches arguments for building a convolution (up or down)
  
  Arguments:
      conv_opt
      index in sequential order (as they come in the config)
      flow "UP" or "DOWN"
code: def _fetch_arguments(self, conv_opt, index, flow):
      """Fetches arguments for building a convolution (up or down)
  
      Arguments:
          conv_opt
          index in sequential order (as they come in the config)
          flow "UP" or "DOWN"
      """
      args = self._fetch_arguments_from_list(conv_opt, index)
      args["conv_cls"] = self._factory_module.get_module(flow)
      args["index"] = index
      return args
  
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 10 for calling the UnwrappedUnetBasedModel obj's underlying member method code with example usage: obj._fetch_arguments(conv_opt, index, flow) and returns: args