class_docstr: Stopnet signalling decoder to stop inference.
  Args:
      in_features (int): feature dimension of input.
class_docstr_tok: ["Stopnet","signalling","decoder","to","stop","inference",".","Args",":","in_features","(","int",")",":","feature","dimension","of","input","."]
code: def forward(self, inputs):
      outputs = self.dropout(inputs)
      outputs = self.linear(outputs)
      return outputs
  
imports: import torch
  from torch import nn
  from .common_layers import Prenet
  from .attentions import init_attn
label_desc: Use image node_id 2 for calling the StopNet obj's underlying member method code with example usage: obj.forward(inputs) and returns: outputs