class_docstr: DeNoising AutoEncoder configuration.
  
  Args:
      noise_strategy (str): Defines what kind of noise we are introducing to samples. `swap` - Swap noise
              is when we replace values of a feature with random permutations of the same feature. `zero` - Zero
              noise is when we replace values of a feature with zeros. Defaults to swap. Choices are:
              [`swap`,`zero`].
  
      noise_probabilities (Dict[str, float]): Dict of individual probabilities to corrupt the input
              features with swap/zero noise. Key should be the feature name and if any feature is missing, the
              default_noise_probability is used. Default is an empty dict()
  
      default_noise_probability (float): Default probability to corrupt the input features with swap/zero
              noise. For features for which noise_probabilities does not define a probability. Default is 0.8
  
      loss_type_weights (Optional[List[float]]): Weights to be used for the loss function in the order
              [binary, categorical, numerical]. If None, will use the default weights using a formula. eg. for
              binary, default weight will be n_binary/n_features. Defaults to None
  
      mask_loss_weight (float): Weight to be used for the loss function for the masked features. Defaults
              to 1.0
  
      max_onehot_cardinality (int): Maximum cardinality of one-hot encoded categorical features. Any
              categorical feature with cardinality>max_onehot_cardinality will be embedded in a learned
              embedding space and others will be converted to a one hot representation. If set to 0, will use
              the embedding strategy for all categorical feature. Default is 4
  
      include_input_features_inference (bool): If True, will include the input features along with the
              learned features while fine tuning. Defaults to False
  
      encoder_config (Optional[pytorch_tabular.config.config.ModelConfig]): The config of the encoder to
              be used for the model. Should be one of the model configs defined in PyTorch Tabular
  
      decoder_config (Optional[pytorch_tabular.config.config.ModelConfig]): The config of decoder to be
              used for the model. Should be one of the model configs defined in PyTorch Tabular. Defaults to
              nn.Identity
  
      embedding_dims (Optional[List]): The dimensions of the embedding for each categorical column as a
              list of tuples (cardinality, embedding_dim). If left empty, will infer using the cardinality of
              the categorical column using the rule min(50, (x + 1) // 2)
  
      embedding_dropout (float): Dropout to be applied to the Categorical Embedding. Defaults to 0.1
  
      batch_norm_continuous_input (bool): If True, we will normalize the continuous layer by passing it
              through a BatchNorm layer.
  
      learning_rate (float): The learning rate of the model. Defaults to 1e-3
  
      seed (int): The seed for reproducibility. Defaults to 42
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code: def __post_init__(self):
      assert hasattr(
          self.encoder_config, "_backbone_name"
      ), "encoder_config should have a _backbone_name attribute"
      if self.decoder_config is not None:
          assert hasattr(
              self.decoder_config, "_backbone_name"
          ), "decoder_config should have a _backbone_name attribute"
      super().__post_init__()
  
imports: from dataclasses import dataclass, field
  from typing import Dict, List, Optional
  from pytorch_tabular.config import SSLModelConfig
label_desc: Use image node_id 1 for calling the DenoisingAutoEncoderConfig obj's underlying member method code with example usage: obj.__post_init__() without return types