class_docstr: Enables different values in a categorical features to have different embeddings.
class_docstr_tok: ["Enables","different","values","in","a","categorical","features","to","have","different","embeddings","."]
code: def embedded_cat_dim(self):
      return sum(
          [
              embd_dim
              for i, (_, embd_dim) in enumerate(
                  self.categorical_embedding_dims
              )
              if i in self._embedding_feat_idx
          ]
      )
  
file_path: pytorch_tabular/src/pytorch_tabular/ssl_models/common/layers.py
imports: from collections import OrderedDict
  from typing import Any, Dict, Tuple
  import torch
  from torch import nn
  from pytorch_tabular.models.common.layers.batch_norm import BatchNorm1d
  from pytorch_tabular.ssl_models.common.utils import OneHot
label_desc: Use image node_id 2 for calling the MixedEmbedding1dLayer obj's underlying member method code with example usage: obj.embedded_cat_dim() and returns: sum