code: def __init__(
      self,
      input_size: int = 1,
      num_classes: int = None,
      decoder_config=None,
      **kwargs,
  ):
      super().__init__()
      self.config = decoder_config
  
      logger.debug(f" {self.name}")
      self.input_size = input_size
      self.num_classes = num_classes
  
imports: import logging
  from functools import partial
  import torch
  from ludwig.api_annotations import DeveloperAPI
  from ludwig.constants import BINARY, CATEGORY, CATEGORY_DISTRIBUTION, LOSS, NUMBER, SET, TIMESERIES, TYPE, VECTOR
  from ludwig.decoders.base import Decoder
  from ludwig.decoders.registry import register_decoder
  from ludwig.schema.decoders.base import ClassifierConfig, PassthroughDecoderConfig, ProjectorConfig, RegressorConfig
  from ludwig.utils.torch_utils import Dense, get_activation
label_desc: Use image node_id 1 to create a new PassthroughDecoder object from inherited base classes: Decoder with example: obj = PassthroughDecoder(input_size, num_classes, decoder_config)