code: def _create_branch(
      d_model: int, nhead: int, metric: str
  ) -> nn.modules.Module:
      """Create branch module after DPRNN model for predicting metric score.
  
      Args:
          d_model (int): The number of expected features in the input.
          nhead (int): Number of heads in the multi-head attention model.
          metric (str): The metric name to predict.
  
      Returns:
          (nn.Module): Returned module to predict corresponding metric score.
      """
      layer1 = nn.TransformerEncoderLayer(
          d_model, nhead, d_model * 4, dropout=0.0, batch_first=True
      )
      layer2 = AutoPool()
      if metric == "stoi":
          layer3 = nn.Sequential(
              nn.Linear(d_model, d_model),
              nn.PReLU(),
              nn.Linear(d_model, 1),
              RangeSigmoid(),
          )
      elif metric == "pesq":
          layer3 = nn.Sequential(
              nn.Linear(d_model, d_model),
              nn.PReLU(),
              nn.Linear(d_model, 1),
              RangeSigmoid(val_range=PESQRange),
          )
      else:
          layer3: nn.modules.Module = nn.Sequential(
              nn.Linear(d_model, d_model),
              nn.PReLU(),
              nn.Linear(d_model, 1),
          )
      return nn.Sequential(layer1, layer2, layer3)
  
imports: import math
  from typing import List, Optional, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling a global function with example usage: _create_branch(d_model, nhead, metric) and returns: nn