code: def covariance(y_true, y_pred, axis=None):
      centered_true = T.mean(y_true, axis=axis)
      centered_pred = T.mean(y_pred, axis=axis)
  
      if axis is not None:
          # TODO: write a function to do this..
          shape = list(T.shape(y_true))
          shape[axis] = 1
          centered_true = T.reshape(centered_true, shape)
          shape = list(T.shape(y_pred))
          shape[axis] = 1
          centered_pred = T.reshape(centered_pred, shape)
  
      return T.mean(
          (y_true - centered_true) * (y_pred - centered_pred), axis=axis
      )
  
file_path: tensorly/tensorly/metrics/regression.py
imports: from ..None import backend
label_desc: Use image node_id 5 for calling a global function with example usage: covariance(y_true, y_pred, axis) and returns: T