class_docstr: Create a Unet unwrapped generator
class_docstr_tok: ["Create","a","Unet","unwrapped","generator"]
code: def _save_upsample(self, up_conv):
      upsample_op = getattr(up_conv, "upsample_op", None)
      if upsample_op:
          self._spatial_ops_dict["upsample_op"].append(upsample_op)
  
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 2 for calling the UnwrappedUnetBasedModel obj's underlying member method code with example usage: obj._save_upsample(up_conv) without return types