code: def forward(self, src):
      # src [N, T, F], [512, 60, 6]
      src = self.feature_layer(src)  # [512, 60, 8]
  
      # src [N, T, F] --> [T, N, F], [60, 512, 8]
      src = src.transpose(1, 0)  # not batch first
  
      mask = None
  
      src = self.pos_encoder(src)
      output = self.transformer_encoder(src, mask)  # [60, 512, 8]
  
      # [T, N, F] --> [N, T*F]
      output = self.decoder_layer(
          output.transpose(1, 0)[:, -1, :]
      )  # [512, 1]
  
      return output.squeeze()
  
imports: from __future__ import division
  from __future__ import print_function
  import numpy
  import pandas
  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 torch.utils.data import DataLoader
  from ...model.base import Model
  from ...data.dataset import DatasetH
  from ...data.dataset.handler import DataHandlerLP
label_desc: Use image node_id 2 for calling the Transformer obj's underlying member method code with example usage: obj.forward(src) and returns: output