class_docstr: DANet configuration.
  
  Args:
      n_layers (int): Number of Blocks in the DANet. 8, 20, 32 are configurations
          the paper evaluated. Defaults to 8
  
      abstlay_dim_1 (int): The dimension for the intermediate output in the
              first ABSTLAY layer in a Block. Defaults to 32
  
      abstlay_dim_2 (int): The dimension for the intermediate output in the
              second ABSTLAY layer in a Block. Defaults to 64
  
      k (int): The number of feature groups in the ABSTLAY layer. Defaults to 5
  
      dropout_rate (float): Dropout to be applied in the Block. Defaults to 0.1
  
      task (str): Specify whether the problem is regression or classification. `backbone` is a task which
              considers the model as a backbone to generate features. Mostly used internally for SSL and related
              tasks. Choices are: [`regression`,`classification`,`backbone`].
  
      head (Optional[str]): The head to be used for the model. Should be one of the heads defined in
              `pytorch_tabular.models.common.heads`. Defaults to  LinearHead. Choices are:
              [`None`,`LinearHead`,`MixtureDensityHead`].
  
      head_config (Optional[Dict]): The config as a dict which defines the head. If left empty, will be
              initialized as default linear head.
  
      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 0.0
  
      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.
  
      loss (Optional[str]): The loss function to be applied. By Default, it is MSELoss for regression and
              CrossEntropyLoss for classification. Unless you are sure what you are doing, leave it at MSELoss
              or L1Loss for regression and CrossEntropyLoss for classification
  
      metrics (Optional[List[str]]): the list of metrics you need to track during training. The metrics
              should be one of the functional metrics implemented in ``torchmetrics``. By default, it is
              accuracy if classification and mean_squared_error for regression
  
      metrics_params (Optional[List]): The parameters to be passed to the metrics function. `task` is forced to
              be `multiclass` because the multiclass version can handle binary as well and for simplicity we are
              only using `multiclass`.
  
      metrics_prob_input (Optional[List]): Is a mandatory parameter for classification metrics defined in the config.
          This defines whether the input to the metric function is the probability or the class. Length should be
          same as the number of metrics. Defaults to None.
  
      target_range (Optional[List]): The range in which we should limit the output variable. Currently
              ignored for multi-target regression. Typically used for Regression problems. If left empty, will
              not apply any restrictions
  
      seed (int): The seed for reproducibility. Defaults to 42
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code: def __post_init__(self):
      if self.abstlay_dim_2 is None:
          self.abstlay_dim_2 = self.abstlay_dim_1 * 2
      return super().__post_init__()
  
imports: from dataclasses import dataclass, field
  from typing import Optional
  from pytorch_tabular.config import ModelConfig
label_desc: Use image node_id 1 for calling the DANetConfig obj's underlying member method code with example usage: obj.__post_init__() and returns: super