code: def __init__(
      self,
      input_size,
      output_size,
      use_bias=True,
      weights_initializer="xavier_uniform",
      bias_initializer="zeros",
  ):
      super().__init__()
      self.dense = nn.Linear(
          in_features=input_size,
          out_features=output_size,
          bias=use_bias,
      )
      weights_initializer = initializer_registry[weights_initializer]
      weights_initializer(self.dense.weight)
  
      if use_bias:
          bias_initializer = initializer_registry[bias_initializer]
          bias_initializer(self.dense.bias)
  
imports: import math
  import os
  import warnings
  from abc import abstractmethod
  from functools import lru_cache
  from typing import List, Optional, Tuple, Union
  import torch
  from torch import nn
  from torch.nn import Module, ModuleDict
  from ludwig.api_annotations import DeveloperAPI
  from ludwig.constants import ENCODER_OUTPUT
  from ludwig.utils.strings_utils import SpecialSymbol
label_desc: Use image node_id 1 to create a new Dense object from inherited base classes: LudwigModule with example: obj = Dense(input_size, output_size, use_bias, weights_initializer, bias_initializer)