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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 _validate_row_label(dataset, label=None, default_label='__id'):
 """ Validate a row label column. If the row label is not specified, a column is created with row numbers, named with the string in the `default_label` parameter. Parameters dataset : SFrame Input dataset. label : str, optional Name of the column containing row labels. default_label : str, optional The default column name if `label` is not specified. A column with row numbers is added to the output SFrame in this case. Returns ------- dataset : SFrame The input dataset, but with an additional row label column, *if* there was no input label. label : str The final label column name. """

    ## If no label is provided, set it to be a default and add a row number to
    #  dataset. Check that this new name does not conflict with an existing
    #  name.
    if not label:

        ## Try a bunch of variations of the default label to find one that's not
        #  already a column name.
        label_name_base = default_label
        label = default_label
        i = 1

        while label in dataset.column_names():
            label = label_name_base + '.{}'.format(i)
            i += 1

        dataset = dataset.add_row_number(column_name=label)

    ## Validate the label name and types.
    if not isinstance(label, str):
        raise TypeError("The row label column name '{}' must be a string.".format(label))

    if not label in dataset.column_names():
        raise ToolkitError("Row label column '{}' not found in the dataset.".format(label))

    if not dataset[label].dtype in (str, int):
        raise TypeError("Row labels must be integers or strings.")

    ## Return the modified dataset and label
    return dataset, label