code: def get_pool_and_conv_props_poolLateV2(
      patch_size, min_feature_map_size, max_numpool, spacing
  ):
      initial_spacing = deepcopy(spacing)
      reach = max(initial_spacing)
      dim = len(patch_size)
  
      num_pool_per_axis = get_network_numpool(
          patch_size, max_numpool, min_feature_map_size
      )
  
      net_num_pool_op_kernel_sizes = []
      net_conv_kernel_sizes = []
      net_numpool = max(num_pool_per_axis)
  
      current_spacing = spacing
      for p in range(net_numpool):
          reached = [
              current_spacing[i] / reach > 0.5 for i in range(dim)
          ]
          pool = [
              2 if num_pool_per_axis[i] + p >= net_numpool else 1
              for i in range(dim)
          ]
          if all(reached):
              conv = [3] * dim
          else:
              conv = [3 if not reached[i] else 1 for i in range(dim)]
          net_num_pool_op_kernel_sizes.append(pool)
          net_conv_kernel_sizes.append(conv)
          current_spacing = [
              i * j for i, j in zip(current_spacing, pool)
          ]
  
      net_conv_kernel_sizes.append([3] * dim)
  
      must_be_divisible_by = get_shape_must_be_divisible_by(
          num_pool_per_axis
      )
      patch_size = pad_shape(patch_size, must_be_divisible_by)
  
      return (
          num_pool_per_axis,
          net_num_pool_op_kernel_sizes,
          net_conv_kernel_sizes,
          patch_size,
          must_be_divisible_by,
      )
  
file_path: PaddleSeg/contrib/MedicalSeg/tools/preprocess_utils/experiment_utils.py
imports: import numpy
  from copy import deepcopy
label_desc: Use image node_id 4 for calling a global function with example usage: get_pool_and_conv_props_poolLateV2(patch_size, min_feature_map_size, max_numpool, spacing) and returns: num_pool_per_axis,  net_num_pool_op_kernel_sizes,  net_conv_kernel_sizes,  patch_size,  must_be_divisible_by