class_docstr: create a backbone-based generator:
  This is simply an encoder
  (can be used in classification, regression, metric learning and so one)
docstr: Converts from a dict of lists, to a list of dicts
          
docstr_tok: ["Converts","from","a","dict","of","lists",",","to","a","list","of","dicts"]
code: def _flatten_compact_options(self, opt):
      """Converts from a dict of lists, to a list of dicts"""
      flattenedOpts = []
  
      for index in range(int(1e6)):
          try:
              flattenedOpts.append(
                  DictConfig(
                      self._fetch_arguments_from_list(opt, index)
                  )
              )
          except IndexError:
              break
  
      return flattenedOpts
  
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 8 for calling the BackboneBasedModel obj's underlying member method code with example usage: obj._flatten_compact_options(opt) and returns: flattenedOpts