class_docstr: Transformer model for tabular data, can also be used for semi-supervised learning.
  This is the internal transformer model embedding that will have further fully connected layers (TabNet) to
  actually produce predictions.
code: def init_input(self, input):
      feats = [
          init(input[:, i])
          for i, init in enumerate(self.cat_initializers.values())
      ]
  
      if self.readout == "readout_emb":
          readout_emb = self.readout_emb.expand_as(feats[0])
          feat_embs = torch.stack(
              [readout_emb] + feats, dim=0
          )  # (n_feat_embeddings + 1) x batch x hidden_dim
      else:
          feat_embs = torch.stack(
              feats, dim=0
          )  # n_feat_embeddings x batch x hidden_dim
      return feat_embs
  
file_path: autogluon/tabular/src/autogluon/tabular/models/tab_transformer/tab_transformer.py
imports: import copy
  import torch
  import torch.nn
  from .tab_model_base import TabModelBase
label_desc: Use image node_id 2 for calling the TabTransformer obj's underlying member method code with example usage: obj.init_input(input) and returns: feat_embs