code: def _same_padding(input_size, kernel_size, stride):
      if input_size % stride == 0:
          padding = max(kernel_size - stride, 0)
      else:
          padding = max(kernel_size - (input_size % stride), 0)
      return padding // 2, padding - padding // 2
  
imports: import numpy
  import pytest
  from absl.testing import parameterized
  from numpy.lib.stride_tricks import as_strided
  from keras import constraints
  from keras import layers
  from keras import testing
label_desc: Use image node_id 1 for calling a global function with example usage: _same_padding(input_size, kernel_size, stride) and returns: unknown,  unknown