def METHOD_NAME(data, sampling_rate=1000, **kwargs):
    """**EDA Analysis on Interval-Related Data**
    Performs EDA analysis on longer periods of data (typically > 10 seconds), such as resting-state
    data.
    Parameters
    ----------
    data : Union[dict, pd.DataFrame]
        A DataFrame containing the different processed signal(s) as different columns, typically
        generated by :func:`eda_process` or :func:`bio_process`. Can also take a dict containing
        sets of separately processed DataFrames.
    sampling_rate : int
        The sampling frequency of ``ecg_signal`` (in Hz, i.e., samples/second). Defaults to 1000.
    **kwargs
        Other arguments to be passed to the functions.
    Returns
    -------
    DataFrame
        A dataframe containing the analyzed EDA features. The analyzed
        features consist of the following:
        * ``"SCR_Peaks_N"``: the number of occurrences of Skin Conductance Response (SCR).
        * ``"SCR_Peaks_Amplitude_Mean"``: the mean amplitude of the SCR peak occurrences.
        * ``"EDA_Tonic_SD"``: the mean amplitude of the SCR peak occurrences.
        * ``"EDA_Sympathetic"``: see :func:`eda_sympathetic` (only computed if signal duration
          > 64 sec).
        * ``"EDA_Autocorrelation"``: see :func:`eda_autocor` (only computed if signal duration
          > 30 sec).
    See Also
    --------
    .bio_process, eda_eventrelated
    Examples
    ----------
    .. ipython:: python
      import neurokit2 as nk
      # Download data
      data = nk.data("bio_resting_8min_100hz")
      # Process the data
      df, info = nk.eda_process(data["EDA"], sampling_rate=100)
      # Single dataframe is passed
      nk.eda_intervalrelated(df, sampling_rate=100)
      epochs = nk.epochs_create(df, events=[0, 25300], sampling_rate=100, epochs_end=20)
      nk.eda_intervalrelated(epochs, sampling_rate=100)
    """
    # Format input
    if isinstance(data, pd.DataFrame):
        results = _eda_intervalrelated(data, sampling_rate=sampling_rate, **kwargs)
        results = pd.DataFrame.from_dict(results, orient="index").T
    elif isinstance(data, dict):
        results = {}
        for index in data:
            results[index] = {}  # Initialize empty container
            # Add label info
            results[index]["Label"] = data[index]["Label"].iloc[0]
            results[index] = _eda_intervalrelated(
                data[index], results[index], sampling_rate=sampling_rate, **kwargs
            )
        results = pd.DataFrame.from_dict(results, orient="index")
    return results