class_docstr: A trivial non-recurrent encoder. Simply applies mean pooling.
  
  Args:
     num_layers (int): number of replicated layers
     embeddings (onmt.modules.Embeddings): embedding module to use
code: def __init__(self, num_layers, embeddings):
      super(MeanEncoder, self).__init__()
      self.num_layers = num_layers
      self.embeddings = embeddings
  
file_path: OpenNMT-py/onmt/encoders/mean_encoder.py
imports: from onmt.encoders.encoder import EncoderBase
  from onmt.utils.misc import sequence_mask
  import torch
label_desc: Use image node_id 1 to create a new MeanEncoder object from inherited base classes: EncoderBase with example: obj = MeanEncoder(num_layers, embeddings)