code: def __init__(
      self,
      input_size,
      use_bias=True,
      weights_initializer="xavier_uniform",
      bias_initializer="zeros",
      decoder_config=None,
      **kwargs,
  ):
      super().__init__()
      self.config = decoder_config
  
      logger.debug(f" {self.name}")
  
      logger.debug("  Dense")
  
      self.dense = Dense(
          input_size=input_size,
          output_size=1,
          use_bias=use_bias,
          weights_initializer=weights_initializer,
          bias_initializer=bias_initializer,
      )
  
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 Regressor object from inherited base classes: Decoder with example: obj = Regressor(input_size, use_bias, weights_initializer, bias_initializer, decoder_config)