"mode". "offsets" is ignored and required to be "None" in
          this case.
    * If "input" is 1D of shape *(N)*, it will be treated as a
      concatenation of multiple bags (sequences).  "offsets" is
      required to be a 1D tensor containing the starting index
      positions of each bag in "input". Therefore, for "offsets"
      of shape *(B)*, "input" will be viewed as having "B" bags.
      Empty bags (i.e., having 0-length) will have returned
      vectors filled by zeros.

classmethod from_pretrained(embeddings, freeze=True, max_norm=None, norm_type=2.0, scale_grad_by_freq=False, mode='mean', sparse=False, include_last_offset=False, padding_idx=None)
  Creates EmbeddingBag instance from given 2-dimensional
  FloatTensor.

  Parameters:
     * **embeddings** (*Tensor*) -- FloatTensor containing weights
       for the EmbeddingBag. First dimension is being passed to
       EmbeddingBag as 'num_embeddings', second as
