class_docstr: Class that defines linalg operation on an array
  
  simplest version as benchmark
  
  linear algebra recipes for multivariate normal and linear
  regression calculations
code: def __init__(self, data=None, sym=None):
      if data is not None:
          if sym is None:
              self.x = np.asarray(data)
              self.m = np.dot(self.x.T, self.x)
          else:
              raise ValueError("data and sym cannot be both given")
      elif sym is not None:
          self.m = np.asarray(sym)
          self.x = np.eye(*self.m.shape)  # default
  
      else:
          raise ValueError("either data or sym need to be given")
  
imports: import numpy
  from scipy import linalg
  from statsmodels.tools.decorators import cache_readonly
label_desc: Use image node_id 1 to create a new PlainMatrixArray object  with example: obj = PlainMatrixArray(data, sym)