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 __init__(self, in_features):
      super().__init__()
      self.dropout = nn.Dropout(0.1)
      self.linear = nn.Linear(in_features, 1)
      torch.nn.init.xavier_uniform_(
          self.linear.weight,
          gain=torch.nn.init.calculate_gain("linear"),
      )
  
imports: import torch
  from torch import nn
  from .attentions import init_attn
  from .common_layers import Prenet
label_desc: Use image node_id 1 to create a new StopNet object from inherited base classes: nn with example: obj = StopNet(in_features)