code: def multivariate_time_series_corr(
      time_series: pd.Series,
      other: pd.DataFrame,
      lags: Union[int, list, np.array],
      method: str = "pearson",
  ) -> pd.DataFrame:
      """
      Compute correlation between a time_series and the lagged values of other
      time series.
  
      Parameters
      ----------
      time_series : pandas Series
          Target time series.
      other : pandas DataFrame
          Time series whose lagged values are correlated to `time_series`.
      lags : int, list, numpy ndarray
          Lags to be included in the correlation analysis.
      method : str, default 'pearson'
          - 'pearson': standard correlation coefficient.
          - 'kendall': Kendall Tau correlation coefficient.
          - 'spearman': Spearman rank correlation.
  
      Returns
      -------
      corr : pandas DataFrame
          Correlation values.
  
      """
  
      if not len(time_series) == len(other):
          raise ValueError(
              "`time_series` and `other` must have the same length."
          )
  
      if not (time_series.index == other.index).all():
          raise ValueError(
              "`time_series` and `other` must have the same index."
          )
  
      if isinstance(lags, int):
          lags = range(lags)
  
      corr = {}
      for col in other.columns:
          lag_values = {}
          for lag in lags:
              lag_values[lag] = other[col].shift(lag)
  
          lag_values = pd.DataFrame(lag_values)
          lag_values.insert(0, None, time_series)
          corr[col] = lag_values.corr(method=method).iloc[1:, 0]
  
      corr = pd.DataFrame(corr)
      corr.index = corr.index.astype("int64")
      corr.index.name = "lag"
  
      return corr
  
imports: from typing import Union, Any, Optional, Tuple, Callable
  import warnings
  import importlib
  import joblib
  import numpy
  import pandas
  import sklearn
  import sklearn.pipeline
  import sklearn.linear_model
  from sklearn.compose import ColumnTransformer
  from sklearn.exceptions import NotFittedError
  import inspect
  from copy import deepcopy
  import skforecast
  from ..exceptions import MissingValuesExogWarning
  from ..exceptions import DataTypeWarning
  from ..exceptions import IgnoredArgumentWarning
  from ..exceptions import SkforecastVersionWarning
label_desc: Use image node_id 24 for calling a global function with example usage: multivariate_time_series_corr(time_series, other, lags, method) and returns: corr