class_docstr: A `BagOfEmbeddingsEncoder` is a simple [`Seq2VecEncoder`](./seq2vec_encoder.md) which simply sums
  the embeddings of a sequence across the time dimension. The input to this module is of shape
  `(batch_size, num_tokens, embedding_dim)`, and the output is of shape `(batch_size, embedding_dim)`.
  
  Registered as a `Seq2VecEncoder` with name "bag_of_embeddings" and "boe".
  
  # Parameters
  
  embedding_dim : `int`, required
      This is the input dimension to the encoder.
  averaged : `bool`, optional (default=`False`)
      If `True`, this module will average the embeddings across time, rather than simply summing
      (ie. we will divide the summed embeddings by the length of the sentence).
code: def forward(
      self, tokens: torch.Tensor, mask: torch.BoolTensor = None
  ):
      if mask is not None:
          tokens = tokens * mask.unsqueeze(-1)
  
      # Our input has shape `(batch_size, num_tokens, embedding_dim)`, so we sum out the `num_tokens`
      # dimension.
      summed = tokens.sum(1)
  
      if self._averaged:
          if mask is not None:
              lengths = get_lengths_from_binary_sequence_mask(mask)
              length_mask = lengths > 0
  
              # Set any length 0 to 1, to avoid dividing by zero.
              lengths = torch.max(lengths, lengths.new_ones(1))
          else:
              lengths = tokens.new_full((1,), fill_value=tokens.size(1))
              length_mask = None
  
          summed = summed / lengths.unsqueeze(-1).float()
  
          if length_mask is not None:
              summed = summed * (length_mask > 0).unsqueeze(-1)
  
      return summed
  
file_path: allennlp/allennlp/modules/seq2vec_encoders/boe_encoder.py
imports: import torch
  from allennlp.modules.seq2vec_encoders.seq2vec_encoder import Seq2VecEncoder
  from allennlp.nn.util import get_lengths_from_binary_sequence_mask
label_desc: Use image node_id 4 for calling the BagOfEmbeddingsEncoder obj's underlying member method code with example usage: obj.forward(tokens, mask) and returns: summed