code: def __init__(
      self,
      latent_dim: int = 384,
      context_length: int = 77,
      *,
      use_triu_attn_mask: bool = False,
      num_layers: int = 12,
      dropout: float = 0.0,
      drop_path_rate: float = 0.0,
      norm_position: str = "pre_norm",
      norm_type: Optional[str] = "layer",
      norm_kwargs: Optional[Dict[str, Any]] = None,
      embedding_norm: Optional[torch.nn.Module] = None,
      embedding_dropout: Optional[float] = None,
      residual_after_norm: bool = False,
      feedforward_dim_ratio: float = 4.0,
      attention_kwargs: Optional[Dict[str, Any]] = None,
      feedforward_kwargs: Optional[Dict[str, Any]] = None,
      use_positional_encoding: bool = True,
      head_pooler: Optional[str] = None,
      no_head_norm: Optional[bool] = None,
      norm_after_head: bool = False,
  ):
      super().__init__()
      if not use_triu_attn_mask:
          self.attention_mask = None
      else:
          mask = torch.empty(
              context_length, context_length, dtype=torch.bool
          )
          mask.fill_(True)
          mask.triu_(1)
          self.register_buffer("attention_mask", mask)
      if attention_kwargs is None:
          attention_kwargs = {}
      attention_kwargs.setdefault("bias", True)
      attention_kwargs.setdefault("num_heads", 6)
      self.encoder = MixedStackedEncoder(
          latent_dim,
          context_length,
          token_mixing_type="attention",
          token_mixing_config=attention_kwargs,
          channel_mixing_config=feedforward_kwargs,
          num_layers=num_layers,
          dropout=dropout,
          drop_path_rate=drop_path_rate,
          norm_position=norm_position,
          norm_type=norm_type,
          norm_kwargs=norm_kwargs,
          embedding_norm=embedding_norm,
          embedding_dropout=embedding_dropout,
          residual_after_norm=residual_after_norm,
          latent_dim_ratio=feedforward_dim_ratio,
          use_head_token=False,
          head_pooler=head_pooler,
          use_positional_encoding=use_positional_encoding,
          is_vision_positional_encoding=False,
          no_head_norm=no_head_norm,
          norm_after_head=norm_after_head,
      )
  
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 1 to create a new TeTEncoder object from inherited base classes: IDLModel with example: obj = TeTEncoder(latent_dim, context_length)