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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 evaluate_accuracy(data_iterator, network):
 """ Measure the accuracy of ResNet Parameters data_iterator: Iter examples of dataset network: ResNet Returns tuple of array element """

    acc = mx.metric.Accuracy()

    # Iterate through data and label
    for i, (data, label) in enumerate(data_iterator):

        # Get the data and label into the GPU
        data = data.as_in_context(ctx[0])
        label = label.as_in_context(ctx[0])

        # Get network's output which is a probability distribution
        # Apply argmax on the probability distribution to get network's classification.
        output = network(data)
        predictions = nd.argmax(output, axis=1)

        # Give network's prediction and the correct label to update the metric
        acc.update(preds=predictions, labels=label)

    # Return the accuracy
    return acc.get()[1]