class_docstr: 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 :class:`~torch.nn.Embedding`, attributes will be randomly
  initialized at module creation time and will be overwritten later
  
  Attributes:
      weight (Tensor): the non-learnable quantized weights of the module of
                       shape :math:`(\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])
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code: def weight(self):
      return self._packed_params._weight()
  
imports: import torch
  import torch.nn
  from torch import Tensor
  from torch._jit_internal import Optional, List
  from .utils import _hide_packed_params_repr
  from .utils import _quantize_weight
label_desc: Use image node_id 7 for calling the Embedding obj's underlying member method code with example usage: obj.weight() and returns: self