class_docstr: input:
   x: B x C x H x W  (C = 2048)
  output: B x nClass x H x W
class_docstr_tok: ["input",":","x",":","B","x","C","x","H","x","W","(","C","=","2048",")","output",":","B","x","nClass","x","H","x","W"]
code: def forward(self, x):
      TFA_out_list = []
      TFA_out_list.append(x)
      level_1_out = self.TFA_level_1(x)
      TFA_out_list.append(level_1_out)
      for i, layer in enumerate(self.TFA_level_list):
          if i == 0:
              output1 = layer(level_1_out)
              TFA_out_list.append(output1)
          else:
              output1 = layer(output1)
              TFA_out_list.append(output1)
      TFA_out = torch.cat(TFA_out_list, 1)
  
      out = self.conv51(
          TFA_out
      )  # B x 4096 x H x W  --> B x 512 x H x W
      out = self.conv6(out)  # B x nClass x H x W
      return out
  
file_path: pywick/pywick/models/segmentation/testnets/tkcnet/tkcnet.py
imports: import torch
  import torch.nn
  from torch.nn.functional import upsample
  from .base import BaseNet
label_desc: Use image node_id 3 for calling the TFAHead obj's underlying member method code with example usage: obj.forward(x) and returns: out