code: def plot_correlations(
      series,
      lags=24,
      alpha=0.05,
      zero_lag=True,
      acf_fft=False,
      acf_adjusted=True,
      pacf_method="ywadjusted",
      suptitle=None,
      series_title=None,
      acf_title="Autocorrelation",
      pacf_title="Partial Autocorrelation",
  ):
      """Plot series and its ACF and PACF values.
  
      Parameters
      ----------
      series : pd.Series
          A time series.
  
      lags : int, default = 24
          Number of lags to include in ACF and PACF plots
  
      alpha : int, default = 0.05
          Alpha value used to set confidence intervals. Alpha = 0.05 results in
          95% confidence interval with standard deviation calculated via
          Bartlett's formula.
  
      zero_lag : bool, default = True
          If True, start ACF and PACF plots at 0th lag
  
      acf_fft : bool,  = False
          Whether to compute ACF via FFT.
  
      acf_adjusted : bool, default = True
          If True, denominator of ACF calculations uses n-k instead of n, where
          n is number of observations and k is the lag.
  
      pacf_method : str, default = 'ywadjusted'
          Method to use in calculation of PACF.
  
      suptitle : str, default = None
          The text to use as the Figure's suptitle.
  
      series_title : str, default = None
          Used to set the title of the series plot if provided. Otherwise, series
          plot has no title.
  
      acf_title : str, default = 'Autocorrelation'
          Used to set title of ACF plot.
  
      pacf_title : str, default = 'Partial Autocorrelation'
          Used to set title of PACF plot.
  
      Returns
      -------
      fig : matplotlib.figure.Figure
  
      axes : np.ndarray
          Array of the figure's Axe objects
  
      Examples
      --------
      >>> from sktime.utils.plotting import plot_correlations
      >>> from sktime.datasets import load_airline
      >>> y = load_airline()
      >>> fig, ax = plot_correlations(y)  # doctest: +SKIP
      """
      _check_soft_dependencies("matplotlib", "statsmodels")
      import matplotlib.pyplot as plt
      from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
  
      series = check_y(series)
      series = convert_to(series, "pd.Series", "Series")
  
      # Setup figure for plotting
      fig = plt.figure(constrained_layout=True, figsize=(12, 8))
      gs = fig.add_gridspec(2, 2)
      f_ax1 = fig.add_subplot(gs[0, :])
      if series_title is not None:
          f_ax1.set_title(series_title)
      f_ax2 = fig.add_subplot(gs[1, 0])
      f_ax3 = fig.add_subplot(gs[1, 1])
  
      # Create expected plots on their respective Axes
      plot_series(series, ax=f_ax1)
      plot_acf(
          series,
          ax=f_ax2,
          lags=lags,
          zero=zero_lag,
          alpha=alpha,
          title=acf_title,
          adjusted=acf_adjusted,
          fft=acf_fft,
      )
      plot_pacf(
          series,
          ax=f_ax3,
          lags=lags,
          zero=zero_lag,
          alpha=alpha,
          title=pacf_title,
          method=pacf_method,
      )
      if suptitle is not None:
          fig.suptitle(suptitle, size="xx-large")
  
      return fig, np.array(fig.get_axes())
  
file_path: sktime/sktime/utils/plotting.py
imports: import math
  from warnings import simplefilter, warn
  import numpy
  import pandas
  from sktime.datatypes import convert_to
  from sktime.utils.validation._dependencies import _check_soft_dependencies
  from sktime.utils.validation.forecasting import check_interval_df, check_y
  from sktime.utils.validation.series import check_consistent_index_type
label_desc: Use image node_id 4 for calling a global function with example usage: plot_correlations(series, lags, alpha, zero_lag, acf_fft, acf_adjusted, pacf_method, suptitle, series_title, acf_title, pacf_title) and returns: fig,  np