updated during training, i.e. it remains as a fixed "pad". For
        a newly constructed EmbeddingBag, the embedding vector at
        "padding_idx" will default to all zeros, but can be updated to
        another value to be used as the padding vector. Note that the
        embedding vector at "padding_idx" is excluded from the
        reduction.
Variables:
      weight (Tensor) -- the learnable weights of the module of
      shape (num_embeddings, embedding_dim) initialized from
      \mathcal{N}(0, 1).
Examples:
  >>> # an EmbeddingBag module containing 10 tensors of size 3
  >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum')
  >>> # a batch of 2 samples of 4 indices each
  >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long)
  >>> offsets = torch.tensor([0, 4], dtype=torch.long)
  >>> embedding_sum(input, offsets)
  tensor([[-0.8861, -5.4350, -0.0523],
          [ 1.1306, -2.5798, -1.0044]])
