class_docstr: A Luong-style attention that also includes the inner product of targets-lookup.
code: def __init__(
      self,
      memories_hidden_dimension: int,
      lookup_hidden_dimension: int,
      output_size: int,
  ) -> None:
      super().__init__()
      self.__Whd = nn.Parameter(
          torch.randn(
              memories_hidden_dimension,
              lookup_hidden_dimension,
              dtype=torch.float,
              requires_grad=True,
          )
      )
      self.__Wout = nn.Linear(
          memories_hidden_dimension + lookup_hidden_dimension,
          output_size,
          bias=False,
      )
  
file_path: ptgnn/ptgnn/neuralmodels/sequence/luongattention.py
imports: import math
  import torch
  from torch import nn
label_desc: Use image node_id 1 to create a new LuongAttentionModule object from inherited base classes: nn with example: obj = LuongAttentionModule(memories_hidden_dimension, lookup_hidden_dimension, output_size)