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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 predict(self, version_name, data):
 """Get prediction results from features instances. Args: version_name: the name of the version used for prediction. data: typically a list of instance to be submitted for prediction. The format of the instance depends on the model. For example, structured data model may require a csv line for each instance. Note that online prediction only works on models that take one placeholder value, such as a string encoding a csv line. Returns: A list of prediction results for given instances. Each element is a dictionary representing output mapping from the graph. An example: [{"predictions": 1, "score": [0.00078, 0.71406, 0.28515]}, {"predictions": 1, "score": [0.00244, 0.99634, 0.00121]}] """

    full_version_name = ('%s/versions/%s' % (self._full_model_name, version_name))
    request = self._api.projects().predict(body={'instances': data},
                                           name=full_version_name)
    request.headers['user-agent'] = 'GoogleCloudDataLab/1.0'
    result = request.execute()
    if 'predictions' not in result:
      raise Exception('Invalid response from service. Cannot find "predictions" in response.')

    return result['predictions']