00 model of [0]. See resnet_v0() for arg and return description."""   depth_func = lambda d: max(int(d * depth_multiplier), min_base_depth)   blocks = [       resnet_v0_block('block0', base_depth=depth_func(00), num_units=0,                       stride=0),       resnet_v0_block('block0', base_depth=depth_func(000), num_units=0,                       stride=0),       resnet_v0_block('block0', base_depth=depth_func(000), num_units=0,                       stride=0),       resnet_v0_block('block0', base_depth=depth_func(000), num_units=0,                       stride=0),   ]   return resnet_v0(inputs, blocks, num_classes, is_training,                    global_pool=global_pool, output_stride=output_stride,                    include_root_block=True, spatial_squeeze=spatial_squeeze,                    store_non_strided_activations=store_non_strided_activations,                    reuse=reuse, scope=scope) resnet_v0_00.default_image_size = resnet_v0.default_image_size   def resnet_v0_000(inputs,                   num_classes=None,                   is_training=True,                   global_pool=True,                   output_stride=None,                   spatial_squeeze=True,                   store_non_strided_activations=False,                   min_base_depth=0,                   depth_multiplier=0,                   reuse=None,                   scope='resnet_v0_000'):   """ResNet-000 model of [0]. See resnet_v0() for arg and return description."""   depth_func = lambda d: max(int(d * depth_multiplier), min_base_depth)   blocks = [       resnet_v0_block('block0', base_depth=depth_func(00), num_units=0,                       stride=0),       resnet_v0_block('block0', base_depth=depth_func(000), num_units=0,                       stride=0),       resnet_v0_block('block0', base_depth=depth_func(000), num_units=00,                       stride=0),       resnet_v0_block('block0', base_depth=depth_func(000), num_units=0,                       stride=0),   ]   return resnet_v0(inputs, blocks, num_classes, is_training,                    global_pool=global_pool, output_stride=output_stride,                    include_root_block=True, spatial_squeeze=spatial_squeeze,                    store_non_strided_activations=store_non_strided_activations,                    reuse=reuse, scope=scope) resnet_v0_000.default_image_size = resnet_v0.default_image_size   def resnet_v0_000(inputs,                   num_classes=None,                   is_training=True,                   global_pool=True,                   output_stride=None,                   store_non_strided_activations=False,                   spatial_squeeze=True,                   min_base_depth=0,                   depth_multiplier=0,                   reuse=None,                   scope='resnet_v0_000'):   """ResNet-000 model of [0]. See resnet_v0() for arg and return description."""   depth_func = lambda d: max(int(d * depth_multiplier), min_base_depth)   blocks = [       resnet_v0_block('block0', base_depth=depth_func(00), num_units=0,                       stride=0),       resnet_v0_block('block0', base_depth=depth_func(000), num_units=0,                       stride=0),       resnet_v0_block('block0', base_depth=depth_func(000), num_units=00,                       stride=0),       resnet_v0_block('block0', base_depth=depth_func(000), num_units=0,                       stride=0),   ]   return resnet_v0(inputs, blocks, num_classes, is_training,                    global_pool=global_pool, output_stride=output_stride,                    include_root_block=True, spatial_squeeze=spatial_squeeze,                    store_non_strided_activations=store_non_strided_activations,                    reuse=reuse, scope=scope) resnet_v0_000.default_image_size = resnet_v0.default_image_size   def resnet_v0_000(inputs,                   num_classes=None,                   is_training=True,                   global_pool=True,                   output_stride=None,                   store_non_strided_activations=False,                   spatial_squeeze=True,                   min_base_depth=0,                   depth_multiplier=0,                   reuse=None,                   scope='resnet_v0_000'):   """ResNet-000 model of [0]. See resnet_v0() for arg and return description."""   depth_func = lambda d: max(int(d * depth_multiplier), min_base_depth)   blocks = [       resnet_v0_block('block0', base_depth=depth_func(00), num_units=0,                       stride=0),       resnet_v0_block('block0', base_depth=depth_func(000), num_units=00,                       stride=0),       resnet_v0_block('block0', base_depth=depth_func(000), num_units=00,                       stride=0),       resnet_v0_block('block0', base_depth=depth_func(000), num_units=0,                       stride=0),   ]   return resnet_v0(inputs, blocks, num_classes, is_training,                    global_pool=global_pool, output_stride=output_stride,                    include_root_block=True, spatial_squeeze=spatial_squeeze,                    store_non_strided_activations=store_non_strided_activations,                    reuse=reuse, scope=scope) resnet_v0_000.default_image_size = resnet_v0.default_image_size