def METHOD_NAME(progress: bool = True, **kwargs: Any) -> SqueezeNet:
    r"""SqueezeNet 1.1 model from the `official SqueezeNet repo
    <https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.1>`_.
    SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters
    than SqueezeNet 1.0, without sacrificing accuracy.
    The required minimum input size of the model is 17x17.
    Args:
        weights (SqueezeNet1_1_Weights, optional): The pretrained weights for the model
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    return _squeezenet("1_1", progress, **kwargs)