class_docstr: Make continuous embeddings and boost learning rate
  
  Args:
      num_embeddings (int): number of embeddings
      embedding_dim (int): embedding dimensions
      scale (float, optional): amount to scale learning rate (Default: 10.0)
      smooth (bool, optional): choose to apply smoothing (Default: ``False``)
class_docstr_tok: ["Make","continuous","embeddings","and","boost","learning","rate","Args",":","num_embeddings","(","int",")",":","number","of","embeddings","embedding_dim","(","int",")",":","embedding","dimensions","scale","(","float",",","optional",")",":","amount","to","scale","learning","rate","(","Default",":","10.0",")","smooth","(","bool",",","optional",")",":","choose","to","apply","smoothing","(","Default",":","``","False","``",")"]
code: def weight(self) -> torch.Tensor:
      return self.embedding.weight * self.scale
  
imports: import math
  import typing
  from typing import Any, Dict, List, Optional
  import torch
  from torch import nn
  from torch.nn import functional
label_desc: Use image node_id 2 for calling the _ScaledEmbedding obj's underlying member method code with example usage: obj.weight() and returns: unknown