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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_loss(ctx, model, pretrained, dataset_name, dtype, ckpt_dir=None, start_step=None):
 """Get model for pre-training."""

    # model
    model, vocabulary = nlp.model.get_model(model,
                                            dataset_name=dataset_name,
                                            pretrained=pretrained, ctx=ctx)

    if not pretrained:
        model.initialize(init=mx.init.Normal(0.02), ctx=ctx)
    model.cast(dtype)

    if ckpt_dir and start_step:
        param_path = os.path.join(ckpt_dir, '%07d.params'%start_step)
        model.load_parameters(param_path, ctx=ctx)
        logging.info('Loading step %d checkpoints from %s.', start_step, param_path)

    model.hybridize(static_alloc=True)

    # losses
    nsp_loss = mx.gluon.loss.SoftmaxCELoss()
    mlm_loss = mx.gluon.loss.SoftmaxCELoss()
    nsp_loss.hybridize(static_alloc=True)
    mlm_loss.hybridize(static_alloc=True)

    return model, nsp_loss, mlm_loss, vocabulary