class_docstr: EESP block, principle: reduce -> split -> transform -> merge
  
  Args:
      in_channels (int): Number of input channels.
      out_channels (int): Number of output channels.
      stride (int, optional): Factor by which we should skip (useful for down-sampling). If 2, then down-samples the feature map by 2. Default: 1.
      branches (int, optional): Number of branches. Default: 4.
      kernel_size_maximum (int, optional): A maximum value of receptive field allowed for EESP block. Default: 7.
      down_method (str, optional): Down sample or not, only support 'avg' and 'esp'(equivalent to stride is 2 or not). Default: 'esp'.
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code: def convert_group_x(self, group_merge, x):
      if x.shape == group_merge.shape:
          group_merge += x
  
      return group_merge
  
imports: import math
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg import utils
  from paddleseg.cvlibs import manager, param_init
  from paddleseg.models import layers
label_desc: Use image node_id 2 for calling the EESP obj's underlying member method code with example usage: obj.convert_group_x(group_merge, x) and returns: group_merge