code: def forward(self, src, mask):
      output = src
      out = src
  
      for i, mod in enumerate(self.layers):
          # [T, N, F] --> [N, T, F] --> [N, F, T]
          out = output.transpose(1, 0).transpose(2, 1)
          out = self.conv[i](out).transpose(2, 1).transpose(1, 0)
  
          output = mod(output + out, src_mask=mask)
  
      return output + out
  
imports: from __future__ import division
  from __future__ import print_function
  import numpy
  import pandas
  from typing import Text, Union
  import copy
  import math
  from ...utils import get_or_create_path
  from ...log import get_module_logger
  import torch
  import torch.nn
  import torch.optim
  from ...model.base import Model
  from ...data.dataset import DatasetH
  from ...data.dataset.handler import DataHandlerLP
  from torch.nn.modules.container import ModuleList
label_desc: Use image node_id 2 for calling the LocalformerEncoder obj's underlying member method code with example usage: obj.forward(src, mask) and returns: unknown