How to use the composeml.offsets.to_offset function in composeml

minimum_data (str): Minimum data before starting search. Default value is first time of index.
            gap (str or int): Time between examples. Default value is window size.
                If an integer, search will start on the first event after the minimum data.
            drop_empty (bool): Whether to drop empty slices. Default value is True.
            label_type (str): The label type can be "continuous" or "categorical". Default value is the inferred label type.
            verbose (bool): Whether to render progress bar. Default value is True.
            *args: Positional arguments for labeling function.
            **kwargs: Keyword arguments for labeling function.

        Returns:
            lt (LabelTimes): Calculated labels with cutoff times.
        """
        assert self.labeling_function, 'missing labeling function(s)'
        self._check_example_count(num_examples_per_instance, gap)
        self.window_size = self.window_size or len(df)
        gap = to_offset(gap or self.window_size)

        is_label_search = isinstance(num_examples_per_instance, dict)
        search = (LabelSearch if is_label_search else ExampleSearch)(num_examples_per_instance)

        records = self._run_search(
            df=df,
            search=search,
            gap=gap,
            min_data=minimum_data,
            drop_empty=drop_empty,
            verbose=verbose,
            *args,
            **kwargs,
        )

        lt = LabelTimes(

def _set_window_size(self, window_size):
        """Set and format initial window size parameter.

        Args:
            window_size (str or int): Duration of each data slice.
                The default value for window size is all future data.
        """
        if window_size is not None:
            window_size = to_offset(window_size)

        self.window_size = window_size

Args:
            df (DataFrame): Data frame to create slices on.
            num_examples_per_instance (int): Number of examples per unique instance of target entity.
            minimum_data (str): Minimum data before starting search. Default value is first time of index.
            gap (str or int): Time between examples. Default value is window size.
                If an integer, search will start on the first event after the minimum data.
            drop_empty (bool): Whether to drop empty slices. Default value is True.
            verbose (bool): Whether to print metadata about slice. Default value is False.

        Returns:
            ds (generator): Returns a generator of data slices.
        """
        self._check_example_count(num_examples_per_instance, gap)
        self.window_size = self.window_size or len(df)
        gap = to_offset(gap or self.window_size)
        groups = self.set_index(df).groupby(self.target_entity)

        if num_examples_per_instance == -1:
            num_examples_per_instance = float('inf')

        for key, df in groups:
            slices = self._slice(df=df, gap=gap, min_data=minimum_data, drop_empty=drop_empty)

            for ds in slices:
                ds.context.target_instance = key
                if verbose: print(ds)
                yield ds

                if ds.context.slice_number >= num_examples_per_instance:
                    break