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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def get_model(name, **kwargs):
 """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_HOME/models' Location for keeping the model parameters. Returns ------- HybridBlock The model. """

    models = {'resnet18_v1': resnet18_v1,
              'resnet34_v1': resnet34_v1,
              'resnet50_v1': resnet50_v1,
              'resnet101_v1': resnet101_v1,
              'resnet152_v1': resnet152_v1,
              'resnet18_v2': resnet18_v2,
              'resnet34_v2': resnet34_v2,
              'resnet50_v2': resnet50_v2,
              'resnet101_v2': resnet101_v2,
              'resnet152_v2': resnet152_v2,
              'vgg11': vgg11,
              'vgg13': vgg13,
              'vgg16': vgg16,
              'vgg19': vgg19,
              'vgg11_bn': vgg11_bn,
              'vgg13_bn': vgg13_bn,
              'vgg16_bn': vgg16_bn,
              'vgg19_bn': vgg19_bn,
              'alexnet': alexnet,
              'densenet121': densenet121,
              'densenet161': densenet161,
              'densenet169': densenet169,
              'densenet201': densenet201,
              'squeezenet1.0': squeezenet1_0,
              'squeezenet1.1': squeezenet1_1,
              'inceptionv3': inception_v3,
              'mobilenet1.0': mobilenet1_0,
              'mobilenet0.75': mobilenet0_75,
              'mobilenet0.5': mobilenet0_5,
              'mobilenet0.25': mobilenet0_25,
              'mobilenetv2_1.0': mobilenet_v2_1_0,
              'mobilenetv2_0.75': mobilenet_v2_0_75,
              'mobilenetv2_0.5': mobilenet_v2_0_5,
              'mobilenetv2_0.25': mobilenet_v2_0_25
             }
    name = name.lower()
    if name not in models:
        raise ValueError(
            'Model %s is not supported. Available options are\n\t%s' % (
                name, '\n\t'.join(sorted(models.keys()))))
    return models[name](**kwargs)