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 get_output_dim(self) -> int:
      return self._embedding_dim
  
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 3 for calling the BagOfEmbeddingsEncoder obj's underlying member method code with example usage: obj.get_output_dim() and returns: self