class_docstr: The ISAHead.
  
  Args:
      num_classes (int): The unique number of target classes.
      in_channels (tuple): The number of input channels.
      isa_channels (int): The channels of ISA Module.
      down_factor (tuple): Divide the height and width dimension to (Ph, PW) groups.
      enable_auxiliary_loss (bool, optional): A bool value indicates whether adding auxiliary loss. Default: True.
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code: def forward(
      self, feat_list: List[paddle.Tensor]
  ) -> List[paddle.Tensor]:
      C3, C4 = feat_list
      x = self.in_conv(C4)
      x_shape = paddle.shape(x)
      P_h, P_w = self.down_factor
      Q_h, Q_w = paddle.ceil(x_shape[2] / P_h).astype(
          "int32"
      ), paddle.ceil(x_shape[3] / P_w).astype("int32")
      pad_h, pad_w = (Q_h * P_h - x_shape[2]).astype("int32"), (
          Q_w * P_w - x_shape[3]
      ).astype("int32")
      if pad_h > 0 or pad_w > 0:
          padding = paddle.concat(
              [
                  pad_w // 2,
                  pad_w - pad_w // 2,
                  pad_h // 2,
                  pad_h - pad_h // 2,
              ],
              axis=0,
          )
          feat = F.pad(x, padding)
      else:
          feat = x
  
      feat = feat.reshape([0, x_shape[1], Q_h, P_h, Q_w, P_w])
      feat = feat.transpose([0, 3, 5, 1, 2, 4]).reshape(
          [-1, self.inter_channels, Q_h, Q_w]
      )
      feat = self.global_relation(feat)
  
      feat = feat.reshape([x_shape[0], P_h, P_w, x_shape[1], Q_h, Q_w])
      feat = feat.transpose([0, 4, 5, 3, 1, 2]).reshape(
          [-1, self.inter_channels, P_h, P_w]
      )
      feat = self.local_relation(feat)
  
      feat = feat.reshape([x_shape[0], Q_h, Q_w, x_shape[1], P_h, P_w])
      feat = feat.transpose([0, 3, 1, 4, 2, 5]).reshape(
          [0, self.inter_channels, P_h * Q_h, P_w * Q_w]
      )
      if pad_h > 0 or pad_w > 0:
          feat = paddle.slice(
              feat,
              axes=[2, 3],
              starts=[pad_h // 2, pad_w // 2],
              ends=[pad_h // 2 + x_shape[2], pad_w // 2 + x_shape[3]],
          )
  
      feat = self.out_conv(paddle.concat([feat, x], axis=1))
      output = self.cls(feat)
  
      if self.enable_auxiliary_loss:
          auxout = self.aux(C3)
          return [output, auxout]
      else:
          return [output]
  
imports: import os
  from typing import Union, List, Tuple
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddlehub.module.module import moduleinfo
  import paddlehub.vision.segmentation_transforms
  from paddlehub.module.cv_module import ImageSegmentationModule
  from isanet_resnet50_voc.resnet import ResNet50_vd
  import isanet_resnet50_voc.layers
label_desc: Use image node_id 2 for calling the ISAHead obj's underlying member method code with example usage: obj.forward(feat_list) and returns: list, list