code: def embedded_dropout(embed, words, dropout=0.1, scale=None):
      if dropout:
          mask = embed.weight.data.new().resize_(
              (embed.weight.size(0), 1)
          ).bernoulli_(1 - dropout).expand_as(embed.weight) / (
              1 - dropout
          )
          masked_embed_weight = mask * embed.weight
      else:
          masked_embed_weight = embed.weight
      if scale:
          masked_embed_weight = (
              scale.expand_as(masked_embed_weight) * masked_embed_weight
          )
  
      padding_idx = embed.padding_idx
      if padding_idx is None:
          padding_idx = -1
  
      X = torch.nn.functional.embedding(
          words,
          masked_embed_weight,
          padding_idx,
          embed.max_norm,
          embed.norm_type,
          embed.scale_grad_by_freq,
          embed.sparse,
      )
      return X
  
label_desc: Use image node_id 1 for calling a global function with example usage: embedded_dropout(embed, words, dropout, scale) and returns: X