class_docstr: create a backbone-based generator:
  This is simply an encoder
  (can be used in classification, regression, metric learning and so one)
docstr: Fetches arguments for building a convolution down
  
  Arguments:
      conv_opt
      index in sequential order (as they come in the config)
      flow "DOWN"
docstr_tok: ["Fetches","arguments","for","building","a","convolution","down","Arguments",":","conv_opt","index","in","sequential","order","(","as","they","come","in","the","config",")","flow","``","DOWN","''"]
code: def _fetch_arguments(self, conv_opt, index, flow="DOWN"):
      """Fetches arguments for building a convolution down
  
      Arguments:
          conv_opt
          index in sequential order (as they come in the config)
          flow "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
  from torch_points3d.datasets.base_dataset import BaseDataset
  from torch_points3d.models.base_model import BaseModel
  from torch_points3d.models.base_architectures import BaseFactory
  from torch_points3d.core.common_modules.base_modules import Identity
  from torch_points3d.core.losses import instantiate_loss_or_miner
  from torch_points3d.utils.config import is_list
label_desc: Use image node_id 7 for calling the BackboneBasedModel obj's underlying member method code with example usage: obj._fetch_arguments(conv_opt, index, flow) and returns: args