code: def forward(
      self,
      batch_idx: int,
      batch: tensor_dict_type,
      state: Optional[TrainerState] = None,
      *,
      apply_head: bool = True,
      clip_skip: int = 0,
      **kwargs: Any,
  ) -> tensor_dict_type:
      net = batch[INPUT_KEY]
      mask = batch.get(MASK_KEY, self.attention_mask)
      if mask is not None:
          t = net.shape[1]
          if t != mask.shape[0]:
              mask = mask[:t, :t]
      net = self.encoder.pre_process(net, **kwargs)
      for i, block in enumerate(self.encoder.mixing_blocks):
          net = block(net, mask=mask)
          if i == len(self.encoder.mixing_blocks) - 1 - clip_skip:
              break
      if apply_head:
          net = self.encoder.post_process(net)
      return {LATENT_KEY: net}
  
file_path: carefree-learn/cflearn/models/nlp/encoder/transformer.py
imports: import torch
  from typing import Any
  from typing import Dict
  from typing import Optional
  from cftool.types import tensor_dict_type
  from .constants import MASK_KEY
  from ....schema import TrainerState
  from ....schema import IDLModel
  from ....constants import INPUT_KEY
  from ....constants import LATENT_KEY
  from ....modules.blocks import MixedStackedEncoder
label_desc: Use image node_id 2 for calling the TeTEncoder obj's underlying member method code with example usage: obj.forward(batch_idx, batch, state) and returns: dict