code: def plot_pacf(
      ts: TimeSeries,
      m: Optional[int] = None,
      max_lag: int = 24,
      method: str = "ywadjusted",
      alpha: float = 0.05,
      fig_size: Tuple[int, int] = (10, 5),
      axis: Optional[plt.axis] = None,
      default_formatting: bool = True,
  ) -> None:
      """
      Plots the Partial ACF of `ts`, highlighting it at lag `m`, with corresponding significance interval.
      Uses :func:`statsmodels.tsa.stattools.pacf` [1]_
  
      Parameters
      ----------
      ts
          The TimeSeries whose ACF should be plotted.
      m
          Optionally, a time lag to highlight on the plot.
      max_lag
          The maximal lag order to consider.
      method
          The method to be used for the PACF calculation.
          - | "yw" or "ywadjusted" : Yule-Walker with sample-size adjustment in
            | denominator for acovf. Default.
          - "ywm" or "ywmle" : Yule-Walker without adjustment.
          - "ols" : regression of time series on lags of it and on constant.
          - "ols-inefficient" : regression of time series on lags using a single
            common sample to estimate all pacf coefficients.
          - "ols-adjusted" : regression of time series on lags with a bias
            adjustment.
          - "ld" or "ldadjusted" : Levinson-Durbin recursion with bias
            correction.
          - "ldb" or "ldbiased" : Levinson-Durbin recursion without bias
            correction.
      alpha
          The confidence interval to display.
      fig_size
          The size of the figure to be displayed.
      axis
          Optionally, an axis object to plot the ACF on.
      default_formatting
          Whether to use the darts default scheme.
  
      References
      ----------
      .. [1] https://www.statsmodels.org/dev/generated/statsmodels.tsa.stattools.pacf.html
      """
  
      ts._assert_univariate()
      raise_if(
          max_lag is None or not (1 <= max_lag < len(ts) // 2),
          "max_lag must be greater than or equal to 1 and less than len(ts)//2.",
      )
      raise_if(
          m is not None and not (0 <= m <= max_lag),
          "m must be greater than or equal to 0 and less than or equal to max_lag.",
      )
      raise_if(
          alpha is None or not (0 < alpha < 1),
          "alpha must be greater than 0 and less than 1.",
      )
  
      r, confint = pacf(
          ts.values(), nlags=max_lag, method=method, alpha=alpha
      )
  
      if axis is None:
          plt.figure(figsize=fig_size)
          axis = plt
  
      for i in range(len(r)):
          axis.plot(
              (i, i),
              (0, r[i]),
              color=(
                  "#b512b8"
                  if m is not None and i == m
                  else "black"
                  if default_formatting
                  else None
              ),
              lw=(1 if m is not None and i == m else 0.5),
          )
  
      # Adjusts the upper band of the confidence interval to center it on the x axis.
      upp_band = [
          confint[lag][1] - r[lag] for lag in range(1, max_lag + 1)
      ]
  
      extra_arguments = {}
      if default_formatting:
          extra_arguments["color"] = "#003DFD"
  
      axis.fill_between(
          np.arange(1, max_lag + 1),
          upp_band,
          [-x for x in upp_band],
          alpha=0.25 if default_formatting else None,
          **extra_arguments,
      )
      axis.plot(
          (0, max_lag + 1),
          (0, 0),
          color="black" if default_formatting else None,
      )
  
imports: import math
  from typing import List, Optional, Sequence, Tuple, Union
  import matplotlib.pyplot
  import numpy
  from scipy.signal import argrelmax
  from scipy.stats import norm
  from statsmodels.tsa.seasonal import MSTL, STL, seasonal_decompose
  from statsmodels.tsa.stattools import acf, adfuller, grangercausalitytests, kpss, pacf
  from darts import TimeSeries
  from darts.logging import get_logger, raise_if, raise_if_not, raise_log
  from .missing_values import fill_missing_values
  from .utils import ModelMode, SeasonalityMode
label_desc: Use image node_id 12 for calling a global function with example usage: plot_pacf(ts, m, max_lag, method, alpha, fig_size, axis, default_formatting) without return types