code: def __init__(
      self,
      input_size,
      output_size,
      use_bias=True,
      weights_initializer="xavier_uniform",
      bias_initializer="zeros",
      activation=None,
      multiplier=1.0,
      clip=None,
      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=output_size,
          use_bias=use_bias,
          weights_initializer=weights_initializer,
          bias_initializer=bias_initializer,
      )
  
      self.activation = get_activation(activation)
      self.multiplier = multiplier
  
      if clip is not None:
          if isinstance(clip, (list, tuple)) and len(clip) == 2:
              self.clip = partial(torch.clip, min=clip[0], max=clip[1])
          else:
              raise ValueError(
                  "The clip parameter of {} is {}. "
                  "It must be a list or a tuple of length 2.".format(
                      self.feature_name, self.clip
                  )
              )
      else:
          self.clip = None
  
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 Projector object from inherited base classes: Decoder with example: obj = Projector(input_size, output_size, use_bias, weights_initializer, bias_initializer, activation, multiplier, clip, decoder_config)