def get_model(name, **kwargs):
    "Returns a pre-defined model by name\n\n    Parameters\n    ----------\n    name : str\n        Name of the model.\n    pretrained : bool\n        Whether to load the pretrained weights for model.\n    classes : int\n        Number of classes for the output layer.\n    ctx : Context, default CPU\n        The context in which to load the pretrained weights.\n    root : str, default '~/.mxnet/models'\n        Location for keeping the model parameters.\n\n    Returns\n    -------\n    HybridBlock\n        The model.\n    "
    models = {'ssd_300_vgg16_atrous_voc': ssd_300_vgg16_atrous_voc, 'ssd_300_vgg16_atrous_coco': ssd_300_vgg16_atrous_coco, 'ssd_512_vgg16_atrous_voc': ssd_512_vgg16_atrous_voc, 'ssd_512_vgg16_atrous_coco': ssd_512_vgg16_atrous_coco, 'ssd_512_resnet18_v1_voc': ssd_512_resnet18_v1_voc, 'ssd_512_resnet50_v1_voc': ssd_512_resnet50_v1_voc, 'ssd_512_resnet50_v1_coco': ssd_512_resnet50_v1_coco, 'ssd_512_resnet101_v2_voc': ssd_512_resnet101_v2_voc, 'ssd_512_resnet152_v2_voc': ssd_512_resnet152_v2_voc, 'ssd_512_mobilenet1_0_voc': ssd_512_mobilenet1_0_voc, 'ssd_512_mobilenet1_0_coco': ssd_512_mobilenet1_0_coco, 'faster_rcnn_resnet50_v2a_voc': faster_rcnn_resnet50_v2a_voc, 'faster_rcnn_resnet50_v2a_coco': faster_rcnn_resnet50_v2a_coco, 'cifar_resnet20_v1': cifar_resnet20_v1, 'cifar_resnet56_v1': cifar_resnet56_v1, 'cifar_resnet110_v1': cifar_resnet110_v1, 'cifar_resnet20_v2': cifar_resnet20_v2, 'cifar_resnet56_v2': cifar_resnet56_v2, 'cifar_resnet110_v2': cifar_resnet110_v2, 'cifar_wideresnet16_10': cifar_wideresnet16_10, 'cifar_wideresnet28_10': cifar_wideresnet28_10, 'cifar_wideresnet40_8': cifar_wideresnet40_8, 'cifar_resnext29_32x4d': cifar_resnext29_32x4d, 'cifar_resnext29_16x64d': cifar_resnext29_16x64d, 'fcn_resnet50_voc': get_fcn_voc_resnet50, 'fcn_resnet101_voc': get_fcn_voc_resnet101, 'fcn_resnet50_ade': get_fcn_ade_resnet50, 'psp_resnet50_ade': get_psp_ade_resnet50, 'resnet18_v1b': resnet18_v1b, 'resnet34_v1b': resnet34_v1b, 'resnet50_v1b': resnet50_v1b, 'resnet101_v1b': resnet101_v1b, 'resnet152_v1b': resnet152_v1b, 'resnet50_v2a': resnet50_v2a, 'resnext50_32x4d': resnext50_32x4d, 'resnext101_32x4d': resnext101_32x4d, 'resnext101_64x4d': resnext101_64x4d, 'se_resnext50_32x4d': se_resnext50_32x4d, 'se_resnext101_32x4d': se_resnext101_32x4d, 'se_resnext101_64x4d': se_resnext101_64x4d, 'senet_52': senet_52, 'senet_103': senet_103, 'senet_154': senet_154, 'se_resnet18_v1': se_resnet18_v1, 'se_resnet34_v1': se_resnet34_v1, 'se_resnet50_v1': se_resnet50_v1, 'se_resnet101_v1': se_resnet101_v1, 'se_resnet152_v1': se_resnet152_v1, 'se_resnet18_v2': se_resnet18_v2, 'se_resnet34_v2': se_resnet34_v2, 'se_resnet50_v2': se_resnet50_v2, 'se_resnet101_v2': se_resnet101_v2, 'se_resnet152_v2': se_resnet152_v2, 'darknet53': darknet53, 'yolo3_416_darknet53_voc': yolo3_416_darknet53_voc, 'yolo3_416_darknet53_coco': yolo3_416_darknet53_coco}
    try:
        net = gluon.model_zoo.vision.get_model(name, **kwargs)
        return net
    except ValueError as e:
        upstream_supported = str(e)
    name = name.lower()
    if (name not in models):
        raise ValueError(('%s\n\t%s' % (upstream_supported, '\n\t'.join(sorted(models.keys())))))
    net = models[name](**kwargs)
    return net
Returns a pre-defined model by name

Parameters
----------
name : str
    Name of the model.
pretrained : bool
    Whether to load the pretrained weights for model.
classes : int
    Number of classes for the output layer.
ctx : Context, default CPU
    The context in which to load the pretrained weights.
root : str, default '~/.mxnet/models'
    Location for keeping the model parameters.

Returns
-------
HybridBlock
    The model.
def get_model(name, **kwargs):
    "Returns a pre-defined model by name\n\n    Parameters\n    ----------\n    name : str\n        Name of the model.\n    pretrained : bool\n        Whether to load the pretrained weights for model.\n    classes : int\n        Number of classes for the output layer.\n    ctx : Context, default CPU\n        The context in which to load the pretrained weights.\n    root : str, default '~/.mxnet/models'\n        Location for keeping the model parameters.\n\n    Returns\n    -------\n    HybridBlock\n        The model.\n    "
    models = {'ssd_300_vgg16_atrous_voc': ssd_300_vgg16_atrous_voc, 'ssd_300_vgg16_atrous_coco': ssd_300_vgg16_atrous_coco, 'ssd_512_vgg16_atrous_voc': ssd_512_vgg16_atrous_voc, 'ssd_512_vgg16_atrous_coco': ssd_512_vgg16_atrous_coco, 'ssd_512_resnet18_v1_voc': ssd_512_resnet18_v1_voc, 'ssd_512_resnet50_v1_voc': ssd_512_resnet50_v1_voc, 'ssd_512_resnet50_v1_coco': ssd_512_resnet50_v1_coco, 'ssd_512_resnet101_v2_voc': ssd_512_resnet101_v2_voc, 'ssd_512_resnet152_v2_voc': ssd_512_resnet152_v2_voc, 'ssd_512_mobilenet1_0_voc': ssd_512_mobilenet1_0_voc, 'ssd_512_mobilenet1_0_coco': ssd_512_mobilenet1_0_coco, 'faster_rcnn_resnet50_v2a_voc': faster_rcnn_resnet50_v2a_voc, 'faster_rcnn_resnet50_v2a_coco': faster_rcnn_resnet50_v2a_coco, 'cifar_resnet20_v1': cifar_resnet20_v1, 'cifar_resnet56_v1': cifar_resnet56_v1, 'cifar_resnet110_v1': cifar_resnet110_v1, 'cifar_resnet20_v2': cifar_resnet20_v2, 'cifar_resnet56_v2': cifar_resnet56_v2, 'cifar_resnet110_v2': cifar_resnet110_v2, 'cifar_wideresnet16_10': cifar_wideresnet16_10, 'cifar_wideresnet28_10': cifar_wideresnet28_10, 'cifar_wideresnet40_8': cifar_wideresnet40_8, 'cifar_resnext29_32x4d': cifar_resnext29_32x4d, 'cifar_resnext29_16x64d': cifar_resnext29_16x64d, 'fcn_resnet50_voc': get_fcn_voc_resnet50, 'fcn_resnet101_voc': get_fcn_voc_resnet101, 'fcn_resnet50_ade': get_fcn_ade_resnet50, 'psp_resnet50_ade': get_psp_ade_resnet50, 'resnet18_v1b': resnet18_v1b, 'resnet34_v1b': resnet34_v1b, 'resnet50_v1b': resnet50_v1b, 'resnet101_v1b': resnet101_v1b, 'resnet152_v1b': resnet152_v1b, 'resnet50_v2a': resnet50_v2a, 'resnext50_32x4d': resnext50_32x4d, 'resnext101_32x4d': resnext101_32x4d, 'resnext101_64x4d': resnext101_64x4d, 'se_resnext50_32x4d': se_resnext50_32x4d, 'se_resnext101_32x4d': se_resnext101_32x4d, 'se_resnext101_64x4d': se_resnext101_64x4d, 'senet_52': senet_52, 'senet_103': senet_103, 'senet_154': senet_154, 'se_resnet18_v1': se_resnet18_v1, 'se_resnet34_v1': se_resnet34_v1, 'se_resnet50_v1': se_resnet50_v1, 'se_resnet101_v1': se_resnet101_v1, 'se_resnet152_v1': se_resnet152_v1, 'se_resnet18_v2': se_resnet18_v2, 'se_resnet34_v2': se_resnet34_v2, 'se_resnet50_v2': se_resnet50_v2, 'se_resnet101_v2': se_resnet101_v2, 'se_resnet152_v2': se_resnet152_v2, 'darknet53': darknet53, 'yolo3_416_darknet53_voc': yolo3_416_darknet53_voc, 'yolo3_416_darknet53_coco': yolo3_416_darknet53_coco}
    try:
        net = gluon.model_zoo.vision.get_model(name, **kwargs)
        return net
    except ValueError as e:
        upstream_supported = str(e)
    name = name.lower()
    if (name not in models):
        raise ValueError(('%s\n\t%s' % (upstream_supported, '\n\t'.join(sorted(models.keys())))))
    net = models[name](**kwargs)
    return net