class_docstr: create a backbone-based generator:
  This is simply an encoder
  (can be used in classification, regression, metric learning and so one)
docstr: Fetch the arguments for a single convolution from multiple lists
  of arguments - for models specified in the compact format.
docstr_tok: ["Fetch","the","arguments","for","a","single","convolution","from","multiple","lists","of","arguments","-","for","models","specified","in","the","compact","format","."]
code: def _fetch_arguments_from_list(self, opt, index):
      """Fetch the arguments for a single convolution from multiple lists
      of arguments - for models specified in the compact format.
      """
      args = {}
      for o, v in opt.items():
          name = str(o)
          if is_list(v) and len(getattr(opt, o)) > 0:
              if name[-1] == "s" and name not in SPECIAL_NAMES:
                  name = name[:-1]
              v_index = v[index]
              if is_list(v_index):
                  v_index = list(v_index)
              args[name] = v_index
          else:
              if is_list(v):
                  v = list(v)
              args[name] = v
      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 6 for calling the BackboneBasedModel obj's underlying member method code with example usage: obj._fetch_arguments_from_list(opt, index) and returns: args