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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 _inner_take_over_or_update(self, full_values=None, current_values=None, value_indices=None):
 """ This is for automatic updates of values in the inner loop of missing data handling. Both arguments are dictionaries and the values in full_values will be updated by the current_gradients. If a key from current_values does not exist in full_values, it will be initialized to the value in current_values. If there is indices needed for the update, value_indices can be used for that. If value_indices has the same key, as current_values, the update in full_values will be indexed by the indices in value_indices. grads: dictionary of standing gradients (you will have to carefully make sure, that the ordering is right!). The values in here will be updated such that full_values[key] += current_values[key] forall key in full_gradients.keys() gradients: dictionary of gradients in the current set of parameters. value_indices: dictionary holding indices for the update in full_values. if the key exists the update rule is:def df(x):
 full_values[key][value_indices[key]] += current_values[key] """

        for key in current_values.keys():
            if value_indices is not None and key in value_indices:
                index = value_indices[key]
            else:
                index = slice(None)
            if key in full_values:
                try:
                    full_values[key][index] += current_values[key]
                except:
                    full_values[key] += current_values[key]
            else:
                full_values[key] = current_values[key]