def inception_v3(pretrained=True, progress=True, **kwargs):
    r"""Inception v3 model architecture from
    `"Rethinking the Inception Architecture for Computer Vision" <http://arxiv.org/abs/1512.00567>`_.

    .. note::
        **Important**: In contrast to the other models the inception_v3 expects tensors with a size of
        N x 3 x 299 x 299, so ensure your images are sized accordingly.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
        aux_logits (bool): If True, add an auxiliary branch that can improve training.
            Default: *True*
        transform_input (bool): If True, preprocesses the input according to the method with which it
            was trained on ImageNet. Default: *False*
    """
    if pretrained:
        if "transform_input" not in kwargs:
            kwargs["transform_input"] = True
        if "aux_logits" in kwargs:
            original_aux_logits = kwargs["aux_logits"]
            kwargs["aux_logits"] = True
        else:
            original_aux_logits = True
        model = Inception3(**kwargs)
        state_dict = load_state_dict_from_url(
            model_urls["inception_v3_google"], progress=progress
        )
        model.load_state_dict(state_dict)
        if not original_aux_logits:
            model.aux_logits = False
            del model.AuxLogits

        model.fc = nn.Linear(2048, 7)
        return model

    return Inception3(**kwargs)
Inception v3 model architecture from
    `"Rethinking the Inception Architecture for Computer Vision" <http://arxiv.org/abs/1512.00567>`_.

    .. note::
        **Important**: In contrast to the other models the inception_v3 expects tensors with a size of
        N x 3 x 299 x 299, so ensure your images are sized accordingly.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
        aux_logits (bool): If True, add an auxiliary branch that can improve training.
            Default: *True*
        transform_input (bool): If True, preprocesses the input according to the method with which it
            was trained on ImageNet. Default: *False*
    
rmn/models/inception.py