class_docstr: Decoder based on "Convolutional Sequence to Sequence Learning"
  :cite:`DBLP:journals/corr/GehringAGYD17`.
  
  Consists of residual convolutional layers, with ConvMultiStepAttention.
class_docstr_tok: ["Decoder","based","on","``","Convolutional","Sequence","to","Sequence","Learning","''",":","cite",":","`","DBLP",":","journals\/corr\/GehringAGYD17","`",".","Consists","of","residual","convolutional","layers",",","with","ConvMultiStepAttention","."]
code: def detach_state(self):
      self.state["previous_input"] = self.state[
          "previous_input"
      ].detach()
  
file_path: OpenNMT-py/onmt/decoders/cnn_decoder.py
imports: import torch
  import torch.nn
  from onmt.modules import ConvMultiStepAttention, GlobalAttention
  from onmt.utils.cnn_factory import shape_transform, GatedConv
  from onmt.decoders.decoder import DecoderBase
label_desc: Use image node_id 5 for calling the CNNDecoder obj's underlying member method code with example usage: obj.detach_state() without return types