code: def __init__(
      self, model_config, model_type, dataset, modules, *args, **kwargs
  ):
      try:
          default_output_nc = extract_output_nc(model_config)
      except:
          default_output_nc = -1
          log.warning("Could not resolve number of output channels")
      super().__init__(
          model_config,
          model_type,
          dataset,
          modules,
          default_output_nc=default_output_nc,
          *args,
          **kwargs,
      )
  
imports: import os
  import sys
  import queue
  from omegaconf import DictConfig, OmegaConf
  import logging
  from torch_points3d.applications.modelfactory import ModelFactory
  from torch_points3d.modules.RSConv import *
  from torch_points3d.core.base_conv.dense import DenseFPModule
  from torch_points3d.models.base_architectures.unet import UnwrappedUnetBasedModel
  from torch_points3d.datasets.multiscale_data import MultiScaleBatch
  from torch_points3d.core.common_modules.dense_modules import Conv1D
  from torch_points3d.core.common_modules.base_modules import Seq
  from .utils import extract_output_nc
label_desc: Use image node_id 1 to create a new RSConvEncoder object from inherited base classes: RSConvBase with example: obj = RSConvEncoder(model_config, model_type, dataset, modules)