code: def __init__(
      self, dim: int, aux_heads: Optional[List[str]], bias: bool = True
  ):
      super().__init__()
      self.out_dim = 1 + (0 if aux_heads is None else len(aux_heads))
      self.projection = Linear(dim, self.out_dim, bias=bias)
  
file_path: carefree-learn/cflearn/modules/blocks/mixed_stacks/poolers.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from typing import List
  from typing import Optional
  from torch.nn import Module
  from ..customs import Linear
label_desc: Use image node_id 1 to create a new SequencePooler object from inherited base classes: Module with example: obj = SequencePooler(dim, aux_heads, bias)