parameters: (inputs,
                    num_classes=None,
                    is_training=True,
                    global_pool=True,
                    output_stride=None,
                    spatial_squeeze=True,
                    reuse=None,
                    scope='resnet_v1_200')
return_statement: return resnet_v1(inputs, blocks, num_classes, is_training,
                       global_pool=global_pool, output_stride=output_stride,
                       include_root_block=True, spatial_squeeze=spatial_squeeze,
                       reuse=reuse, scope=scope)
docstring: ResNet-200 model of [2]. See resnet_v1() for arg and return description.
docstring_summary: ResNet-200 model of [2]. See resnet_v1() for arg and return description.
docstring_tokens: ResNet - 200 model of [ 2 ] . See resnet_v1 () for arg and return description .
function: def resnet_v1_200(inputs,
                    num_classes=None,
                    is_training=True,
                    global_pool=True,
                    output_stride=None,
                    spatial_squeeze=True,
                    reuse=None,
                    scope='resnet_v1_200'):
      """ResNet-200 model of [2]. See resnet_v1() for arg and return description."""
      blocks = [
          resnet_utils.Block(
              'block1', bottleneck, [(256, 64, 1)] * 2 + [(256, 64, 2)]),
          resnet_utils.Block(
              'block2', bottleneck, [(512, 128, 1)] * 23 + [(512, 128, 2)]),
          resnet_utils.Block(
              'block3', bottleneck, [(1024, 256, 1)] * 35 + [(1024, 256, 2)]),
          resnet_utils.Block(
              'block4', bottleneck, [(2048, 512, 1)] * 3)]
      return resnet_v1(inputs, blocks, num_classes, is_training,
                       global_pool=global_pool, output_stride=output_stride,
                       include_root_block=True, spatial_squeeze=spatial_squeeze,
                       reuse=reuse, scope=scope)
function_tokens: def resnet_v1_200 ( inputs , num_classes = None , is_training = True , global_pool = True , output_stride = None , spatial_squeeze = True , reuse = None , scope = 'resnet_v1_200' ) : blocks = [ resnet_utils . Block ( 'block1' , bottleneck , [ ( 256 , 64 , 1 ) ] * 2 + [ ( 256 , 64 , 2 ) ] ) , resnet_utils . Block ( 'block2' , bottleneck , [ ( 512 , 128 , 1 ) ] * 23 + [ ( 512 , 128 , 2 ) ] ) , resnet_utils . Block ( 'block3' , bottleneck , [ ( 1024 , 256 , 1 ) ] * 35 + [ ( 1024 , 256 , 2 ) ] ) , resnet_utils . Block ( 'block4' , bottleneck , [ ( 2048 , 512 , 1 ) ] * 3 ) ] return resnet_v1 ( inputs , blocks , num_classes , is_training , global_pool = global_pool , output_stride = output_stride , include_root_block = True , spatial_squeeze = spatial_squeeze , reuse = reuse , scope = scope )