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.
docstr: Returns logits for output classes
code: def forward(self, input):
      """
      Returns logits for output classes
      """
      feat_embs = self.init_input(input)
      out = self.run_tfmr(feat_embs)
      return out
  
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 4 for calling the TabTransformer obj's underlying member method code with example usage: obj.forward(input) and returns: out