code: def __init__(self, input_size=1, encoder_config=None, **kwargs):
      super().__init__()
      self.config = encoder_config
  
      logger.debug(f" {self.name}")
      self.input_size = input_size
      self.identity = nn.Identity()
  
imports: import logging
  from typing import Dict, List, Optional, Type, Union
  import torch
  from torch import nn
  from ludwig.api_annotations import DeveloperAPI
  from ludwig.constants import CATEGORY, ENCODER_OUTPUT
  from ludwig.encoders.base import Encoder
  from ludwig.encoders.registry import register_encoder
  from ludwig.encoders.types import EncoderOutputDict
  from ludwig.modules.embedding_modules import Embed
  from ludwig.schema.encoders.base import BaseEncoderConfig
  from ludwig.schema.encoders.category_encoders import CategoricalEmbedConfig, CategoricalOneHotEncoderConfig, CategoricalPassthroughEncoderConfig, CategoricalSparseConfig
label_desc: Use image node_id 1 to create a new CategoricalPassthroughEncoder object from inherited base classes: Encoder with example: obj = CategoricalPassthroughEncoder(input_size, encoder_config)