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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 get_collections(self, level, types=None, variables=None, merge=False, sampling_rate=None, skip_empty=False, **kwargs):
 """Return one or more variable Collections in the BIDS project. Args: level (str):
 The level of analysis to return variables for. Must be one of 'run', 'session', 'subject', or 'dataset'. types (str, list):
 Types of variables to retrieve. All valid values reflect the filename stipulated in the BIDS spec for each kind of variable. Valid values include: 'events', 'physio', 'stim', 'scans', 'participants', 'sessions', and 'regressors'. variables (list):
 Optional list of variables names to return. If None, all available variables are returned. merge (bool):
 If True, variables are merged across all observations of the current level. E.g., if level='subject', variables from all subjects will be merged into a single collection. If False, each observation is handled separately, and the result is returned as a list. sampling_rate (int, str):
 If level='run', the sampling rate to pass onto the returned BIDSRunVariableCollection. skip_empty (bool):
 Whether or not to skip empty Variables (i.e., where there are no rows/records in a file after applying any filtering operations like dropping NaNs). kwargs: Optional additional arguments to pass onto load_variables. """

        from bids.variables import load_variables
        index = load_variables(self, types=types, levels=level,
                               skip_empty=skip_empty, **kwargs)
        return index.get_collections(level, variables, merge,
                                     sampling_rate=sampling_rate)