g.
                                 ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights`
                                 into consideration. ``"mean"`` computes the average of the values
                                 in the bag, ``"max"`` computes the max value over each bag.
                                 Default: ``"mean"``
        sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See
                                 Notes for more details regarding sparse gradients. Note: this option is not
                                 supported when ``mode="max"``.
        include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element
                                      is equivalent to the size of `indices`. This matches the CSR format.
        padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the
                                     gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated
                                     during training, i.e. it remains as a fixed "pad". For a newly constructed
                                     EmbeddingBag, the embedding vector at :attr:`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 :attr:`padding_idx` is excluded from the
                                     reduction.

    Attributes:
        weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)`
                         initialized from :math:`\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)
        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
        >>> embedding_sum(input, offsets)
        tensor([[-0.8861, -5.4350, -0.0523],
                [ 1.1306, -2.5798, -1.0044]])

        >>> # Example with padding_idx
        >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2)
        >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long)
        >>> offsets = torch.tensor([0, 4], dtype=torch.long)
        >>> embedding_sum(input, offsets)
        tensor([[ 0.0000,  0.0000,  0.0000],
                [-0.7082,  3.2145, -2.6251]])

        >>> # An EmbeddingBag can be loaded from an Embedding like so
        >>> embedding = nn.Embedding(10, 3, padding_idx=2)
        >>> embedding_sum = nn.EmbeddingBag.from_pretrained(
                embedding.weight,
                padding_idx=embedding.padding_idx,
                mode='sum')
    )	r