EmbeddingBag
class torch.ao.nn.quantized.EmbeddingBag(num_embeddings, embedding_dim, max_norm=None, norm_type=2.0, scale_grad_by_freq=False, mode='sum', sparse=False, _weight=None, include_last_offset=False, dtype=torch.quint8)
A quantized EmbeddingBag module with quantized packed weights as
   inputs. We adopt the same interface as torch.nn.EmbeddingBag,
   please see
   https://pytorch.org/docs/stable/nn.html#torch.nn.EmbeddingBag for
   documentation.
Similar to "EmbeddingBag", attributes will be randomly initialized
   at module creation time and will be overwritten later
Variables:
      weight (Tensor) -- the non-learnable quantized weights of
      the module of shape (\text{num_embeddings},
      \text{embedding_dim}).
Examples::
      >>> m = nn.quantized.EmbeddingBag(num_embeddings=10, embedding_dim=12, include_last_offset=True, mode='sum')