Embedding
class torch.ao.nn.quantized.Embedding(num_embeddings, embedding_dim, padding_idx=None, max_norm=None, norm_type=2.0, scale_grad_by_freq=False, sparse=False, _weight=None, dtype=torch.quint8)
A quantized Embedding module with quantized packed weights as
   inputs. We adopt the same interface as torch.nn.Embedding, please
   see https://pytorch.org/docs/stable/nn.html#torch.nn.Embedding for
   documentation.
Similar to "Embedding", 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.Embedding(num_embeddings=10, embedding_dim=12)
      >>> indices = torch.tensor([9, 6, 5, 7, 8, 8, 9, 2, 8])
      >>> output = m(indices)
      >>> print(output.size())
      torch.Size([9, 12])
classmethod from_float(mod)