human
Can you provide a the Python code you requested given the following:
Complete the below
def get(params, is_training=False, reuse=False, run_projection=True):

    ''''Factory function to get the training/pretraining im->vox model (NIPS16).
    Args:
    params: Different parameters used througout ptn, typically FLAGS (dict).
    is_training: Set to True if while training (boolean).
    reuse: Set as True if sharing variables with a model that has already
    been built (boolean).
    run_projection: Set as False if not interested in mask and projection
    images. Useful in evaluation routine (boolean).
    Returns:
    Model function for network (inputs to outputs).'
    '''
assistant
#Here is the Python code you requested.
    def model(inputs):
        'Model    function    corresponding    to    a    specific    network    architecture.'
        outputs = {}
        encoder_fn = _get_network(params.encoder_name)
        with tf.variable_scope('encoder', reuse=reuse):
            enc_outputs = encoder_fn(inputs['images_1'], params, is_training)
            outputs['ids_1'] = enc_outputs['ids']
        decoder_fn = _get_network(params.decoder_name)
        with tf.variable_scope('decoder', reuse=reuse):
            outputs['voxels_1'] = decoder_fn(outputs['ids_1'], params, is_training)
        if run_projection:
            projector_fn = _get_network(params.projector_name)
            with tf.variable_scope('projector', reuse=reuse):
                outputs['projs_1'] = projector_fn(outputs['voxels_1'], inputs['matrix_1'], params, is_training)
            with tf.variable_scope('oracle', reuse=reuse):
                outputs['masks_1'] = projector_fn(inputs['voxels'], inputs['matrix_1'], params, False)
            for k in range(1, params.step_size):
                with tf.variable_scope('projector', reuse=True):
                    outputs[('projs_%d' % (k + 1))] = projector_fn(outputs['voxels_1'], inputs[('matrix_%d' % (k + 1))], params, is_training)
                with tf.variable_scope('oracle', reuse=True):
                    outputs[('masks_%d' % (k + 1))] = projector_fn(inputs['voxels'], inputs[('matrix_%d' % (k + 1))], params, False)
        return outputs
    return model