parameters: (inputs,
                   num_classes=None,
                   is_training=True,
                   global_pool=True,
                   output_stride=None,
                   reuse=None,
                   scope='resnet_v1_50')
return_statement: return resnet_v1(
        inputs,
        blocks,
        num_classes,
        is_training,
        global_pool,
        output_stride,
        include_root_block=True,
        reuse=reuse,
        scope=scope)
docstring: ResNet-50 model of [1]. See resnet_v1() for arg and return description.
docstring_summary: ResNet-50 model of [1]. See resnet_v1() for arg and return description.
docstring_tokens: ResNet - 50 model of [ 1 ] . See resnet_v1 () for arg and return description .
function: def resnet_v1_50(inputs,
                   num_classes=None,
                   is_training=True,
                   global_pool=True,
                   output_stride=None,
                   reuse=None,
                   scope='resnet_v1_50'):
    """ResNet-50 model of [1]. See resnet_v1() for arg and return description."""
    blocks = [
        resnet_v1_block('block1', base_depth=64, num_units=3, stride=2),
        resnet_v1_block('block2', base_depth=128, num_units=4, stride=2),
        resnet_v1_block('block3', base_depth=256, num_units=6, stride=2),
        resnet_v1_block('block4', base_depth=512, num_units=3, stride=1),
    ]
    return resnet_v1(
        inputs,
        blocks,
        num_classes,
        is_training,
        global_pool,
        output_stride,
        include_root_block=True,
        reuse=reuse,
        scope=scope)
function_tokens: def resnet_v1_50 ( inputs , num_classes = None , is_training = True , global_pool = True , output_stride = None , reuse = None , scope = 'resnet_v1_50' ) : blocks = [ resnet_v1_block ( 'block1' , base_depth = 64 , num_units = 3 , stride = 2 ) , resnet_v1_block ( 'block2' , base_depth = 128 , num_units = 4 , stride = 2 ) , resnet_v1_block ( 'block3' , base_depth = 256 , num_units = 6 , stride = 2 ) , resnet_v1_block ( 'block4' , base_depth = 512 , num_units = 3 , stride = 1 ) , ] return resnet_v1 ( inputs , blocks , num_classes , is_training , global_pool , output_stride , include_root_block = True , reuse = reuse , scope = scope )