code: def corr2cov(corr, std):
      """convert correlation matrix to covariance matrix
  
      Parameters
      ----------
      corr : ndarray, (k_vars, k_vars)
          correlation matrix
      std : ndarray, (k_vars,) or scalar
          standard deviation for the vector of random variables. If scalar, then
          it is assumed that all variables have the same scale given by std.
  
      """
      if np.size(std) == 1:
          std = std * np.ones(corr.shape[0])
      cov = corr * std[:, None] * std[None, :]  # same as outer product
      return cov
  
file_path: statsmodels/statsmodels/sandbox/panel/correlation_structures.py
imports: import numpy
  from statsmodels.regression.linear_model import yule_walker
  from statsmodels.stats.moment_helpers import cov2corr
label_desc: Use image node_id 4 for calling a global function with example usage: corr2cov(corr, std) and returns: cov