docstr: This mixer trains a TabTransformer network (FT variant), using concatenated encoder outputs for each dataset feature as input, to predict the encoded target column representation as output.
  
  Training logic is based on the Neural mixer, please refer to it for more details on each input parameter.
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code: def __init__(
      self,
      stop_after: float,
      target: str,
      dtype_dict: Dict[str, str],
      target_encoder: BaseEncoder,
      fit_on_dev: bool,
      search_hyperparameters: bool,
      train_args: Optional[dict] = None,
  ):
      """
      This mixer trains a TabTransformer network (FT variant), using concatenated encoder outputs for each dataset feature as input, to predict the encoded target column representation as output.
  
      Training logic is based on the Neural mixer, please refer to it for more details on each input parameter.
      """  # noqa
      self.train_args = train_args if train_args else {}
      super().__init__(
          stop_after,
          target,
          dtype_dict,
          target_encoder,
          "FTTransformer",
          False,  # fit_on_dev
          search_hyperparameters,
          n_epochs=self.train_args.get("n_epochs", None),
      )
      self.lr = self.train_args.get("lr")
      self.stable = False
  
file_path: lightwood/lightwood/mixer/tabtransformer.py
imports: from typing import Dict, Optional
  import torch
  from tab_transformer_pytorch import TabTransformer
  from lightwood.helpers.device import get_device_from_name
  from lightwood.data.encoded_ds import EncodedDs
  from lightwood.encoder.base import BaseEncoder
  from lightwood.mixer.neural import Neural
label_desc: Use image node_id 1 to create a new TabTransformerMixer object from inherited base classes: Neural with example: obj = TabTransformerMixer(stop_after, target, dtype_dict, target_encoder, fit_on_dev, search_hyperparameters, train_args)