slim.max_pool2d()

def max_pool2d(inputs,
               kernel_size,
               stride=2,
               padding='VALID',
               data_format=DATA_FORMAT_NHWC,
               outputs_collections=None,
               scope=None):
  if data_format not in (DATA_FORMAT_NCHW, DATA_FORMAT_NHWC):
    raise ValueError('data_format has to be either NCHW or NHWC.')
  with ops.name_scope(scope, 'MaxPool2D', [inputs]) as sc:
    inputs = ops.convert_to_tensor(inputs)
    df = ('channels_first'
          if data_format and data_format.startswith('NC') else 'channels_last')
    layer = pooling_layers.MaxPooling2D(
        pool_size=kernel_size,
        strides=stride,
        padding=padding,
        data_format=df,
        _scope=sc)
    outputs = layer.apply(inputs)
    return utils.collect_named_outputs(outputs_collections, sc, outputs)