class_docstr: The original attention from Badhanau et al. (2014)
  https://arxiv.org/abs/1409.0473 based on a Multi-Layer Perceptron.
  
  The attention score between position i in the encoder and position j in the
  decoder is:
  alpha_ij = V_a * tanh(W_ae * enc_i + W_ad * dec_j + b_a)
code: def prepare_for_onnx_export_(self, **kwargs):
      self.src_length_masking = False
  
file_path: translate/pytorch_translate/attention/mlp_attention.py
imports: import numpy
  import torch
  import torch.nn.functional
  from pytorch_translate.attention import attention_utils, BaseAttention, register_attention
  from pytorch_translate.common_layers import Linear
label_desc: Use image node_id 2 for calling the MLPAttention obj's underlying member method code with example usage: obj.prepare_for_onnx_export_() without return types