class_docstr: Conv attention takes a key matrix, a value matrix and a query vector.
  Attention weight is calculated by key matrix with the query vector
  and sum on the value matrix. And the same operation is applied
  in each decode conv layer.
docstr: Apply mask
code: def apply_mask(self, mask):
      """Apply mask"""
      self.mask = mask
  
file_path: OpenNMT-py/onmt/modules/conv_multi_step_attention.py
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the ConvMultiStepAttention obj's underlying member method code with example usage: obj.apply_mask(mask) without return types