code: def check_branch(branch, mode):
      if mode == HoVerNet.Mode.ORIGINAL:
          ksize = 5
      else:
          ksize = 3
  
      if branch.decoderblock1.conva.kernel_size != (ksize, ksize):
          return True
      if branch.decoderblock1.convf.kernel_size != (1, 1):
          return True
      for block in branch.decoderblock1:
          if isinstance(block, _DenseLayerDecoder):
              if block.layers.conv1.kernel_size != (
                  1,
                  1,
              ) or block.layers.conv2.kernel_size != (ksize, ksize):
                  return True
  
      if branch.decoderblock2.conva.kernel_size != (ksize, ksize):
          return True
      if branch.decoderblock2.convf.kernel_size != (1, 1):
          return True
  
      for block in branch.decoderblock2:
          if isinstance(block, _DenseLayerDecoder):
              if block.layers.conv1.kernel_size != (
                  1,
                  1,
              ) or block.layers.conv2.kernel_size != (ksize, ksize):
                  return True
  
      return False
  
file_path: MONAI/tests/test_hovernet.py
imports: from __future__ import annotations
  import unittest
  import torch
  from parameterized import parameterized
  from monai.networks import eval_mode, train_mode
  from monai.networks.nets import HoVerNet
  from monai.networks.nets.hovernet import _DenseLayerDecoder
  from tests.utils import test_script_save
label_desc: Use image node_id 1 for calling a global function with example usage: check_branch(branch, mode) and returns: False, True, True, True, True, True, True