class_docstr: Sequence pool produces a single embedding from a sequence of embeddings.
  Currently it supports "mean" and "cls".
class_docstr_tok: ["Sequence","pool","produces","a","single","embedding","from","a","sequence","of","embeddings",".","Currently","it","supports","``","mean","''","and","``","cls","''","."]
code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      if self.mode == "cls":
          x = x[:, 0]
      elif self.mode == "mean":
          x = x.mean(1)
      else:
          raise NotImplementedError
      return x
  
imports: import torch
  from torch import nn
label_desc: Use image node_id 2 for calling the SequencePool obj's underlying member method code with example usage: obj.forward(x) and returns: x