class_docstr: Configuration inferred from the data during `fit` of the TabularDatamodule.
  
  Args:
      categorical_dim (int): The number of categorical features
  
      continuous_dim (int): The number of continuous features
  
      output_dim (Optional[int]): The number of output targets
  
      categorical_cardinality (Optional[List[int]]): The number of unique values in categorical features
  
      embedding_dims (Optional[List]): The dimensions of the embedding for each categorical column as a
              list of tuples (cardinality, embedding_dim).
  
      embedded_cat_dim (int): The number of features or dimensions of the embedded categorical features
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code: def __post_init__(self):
      if self.embedding_dims is not None:
          assert all(
              (isinstance(t, Iterable) and len(t) == 2)
              for t in self.embedding_dims
          ), "embedding_dims must be a list of tuples (cardinality, embedding_dim)"
          self.embedded_cat_dim = sum(
              [t[1] for t in self.embedding_dims]
          )
      else:
          self.embedded_cat_dim = 0
  
imports: import os
  import re
  from dataclasses import MISSING, dataclass, field
  from typing import Any, Dict, Iterable, List, Optional
  from omegaconf import OmegaConf
  from pytorch_tabular.models.common import heads
  from pytorch_tabular.utils import get_logger
label_desc: Use image node_id 1 for calling the InferredConfig obj's underlying member method code with example usage: obj.__post_init__() without return types