def METHOD_NAME(include_top=True, weights='imagenet',
               input_tensor=None, input_shape=None,
               pooling=None,
               classes=1000):
    """Instantiates the SqueezeNet architecture.
    """
    if weights not in {'imagenet', None}:
        raise ValueError('The `weights` argument should be either '
                         '`None` (random initialization) or `imagenet` '
                         '(pre-training on ImageNet).')
    if weights == 'imagenet' and classes != 1000:
        raise ValueError('If using `weights` as imagenet with `include_top`'
                         ' as true, `classes` should be 1000')
    input_shape = _obtain_input_shape(input_shape,
                                      default_size=227,
                                      min_size=48,
                                      data_format=K.image_data_format(),
                                      require_flatten=include_top)
    if input_tensor is None:
        img_input = Input(shape=input_shape)
    else:
        if not K.is_keras_tensor(input_tensor):
            img_input = Input(tensor=input_tensor, shape=input_shape)
        else:
            img_input = input_tensor
    x = Convolution2D(64, (3, 3), strides=(2, 2), padding='valid', name='conv1')(img_input)
    x = Activation('relu', name='relu_conv1')(x)
    x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), name='pool1')(x)
    x = fire_module(x, fire_id=2, squeeze=16, expand=64)
    x = fire_module(x, fire_id=3, squeeze=16, expand=64)
    x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), name='pool3')(x)
    x = fire_module(x, fire_id=4, squeeze=32, expand=128)
    x = fire_module(x, fire_id=5, squeeze=32, expand=128)
    x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), name='pool5')(x)
    x = fire_module(x, fire_id=6, squeeze=48, expand=192)
    x = fire_module(x, fire_id=7, squeeze=48, expand=192)
    x = fire_module(x, fire_id=8, squeeze=64, expand=256)
    x = fire_module(x, fire_id=9, squeeze=64, expand=256)
    if include_top:
        # It's not obvious where to cut the network...
        # Could do the 8th or 9th layer... some work recommends cutting earlier layers.
        x = Dropout(0.5, name='drop9')(x)
        x = Convolution2D(classes, (1, 1), padding='valid', name='conv10')(x)
        x = Activation('relu', name='relu_conv10')(x)
        x = GlobalAveragePooling2D()(x)
        x = Activation('softmax', name='loss')(x)
    else:
        if pooling == 'avg':
            x = GlobalAveragePooling2D()(x)
        elif pooling == 'max':
            x = GlobalMaxPooling2D()(x)
        elif pooling == None:
            pass
        else:
            raise ValueError("Unknown argument for 'pooling'=" + pooling)
    # Ensure that the model takes into account
    # any potential predecessors of `input_tensor`.
    if input_tensor is not None:
        inputs = get_source_inputs(input_tensor)
    else:
        inputs = img_input
    model = Model(inputs, x, name='squeezenet')
    # load weights
    if weights == 'imagenet':
        if include_top:
            weights_path = '/tmp/squeezenet_weights_tf_dim_ordering_tf_kernels.h5'
        else:
            weights_path = get_file('squeezenet_weights_tf_dim_ordering_tf_kernels_notop.h5',
                                    WEIGHTS_PATH_NO_TOP,
                                    cache_dir='/tmp/')
        model.load_weights(weights_path)
        if K.backend() == 'theano':
            layer_utils.convert_all_kernels_in_model(model)
        if K.image_data_format() == 'channels_first':
            pass
    return model