code: def convert_padding(params, w_name, scope_name, inputs, layers, weights, names):
      
      print('Converting padding...')
  
      if params['mode'] == 'constant':
          # raise AssertionError('Cannot convert non-constant padding')
  
          if params['value'] != 0.0:
              raise AssertionError('Cannot convert non-zero padding')
  
          if names:
              tf_name = 'PADD' + random_string(4)
          else:
              tf_name = w_name + str(random.random())
  
          # Magic ordering
          padding_name = tf_name
          padding_layer = keras.layers.ZeroPadding2D(
              padding=((params['pads'][2], params['pads'][6]), (params['pads'][3], params['pads'][7])),
              name=padding_name
          )
  
          layers[scope_name] = padding_layer(layers[inputs[0]])
      elif params['mode'] == 'reflect':
  
          def target_layer(x, pads=params['pads']):
              # x = tf.transpose(x, [0, 2, 3, 1])
              layer = tf.pad(x, [[0, 0], [0, 0], [pads[2], pads[6]], [pads[3], pads[7]]], 'REFLECT')
              # layer = tf.transpose(layer, [0, 3, 1, 2])
              return layer
  
          lambda_layer = keras.layers.Lambda(target_layer)
          layers[scope_name] = lambda_layer(layers[inputs[0]])
docs: Convert padding layer.
  
      Args:
          params: dictionary with layer parameters
          w_name: name prefix in state_dict
          scope_name: pytorch scope name
          inputs: pytorch node inputs
          layers: dictionary with keras tensors
          weights: pytorch state_dict
          names: use short names for keras layers