code: def randn(*args):
      """
      Return a random matrix with data from the "standard normal" distribution.
  
      `randn` generates a matrix filled with random floats sampled from a
      univariate "normal" (Gaussian) distribution of mean 0 and variance 1.
  
      Parameters
      ----------
      \\*args : Arguments
          Shape of the output.
          If given as N integers, each integer specifies the size of one
          dimension. If given as a tuple, this tuple gives the complete shape.
  
      Returns
      -------
      Z : matrix of floats
          A matrix of floating-point samples drawn from the standard normal
          distribution.
  
      See Also
      --------
      rand, numpy.random.RandomState.randn
  
      Notes
      -----
      For random samples from the normal distribution with mean ``mu`` and
      standard deviation ``sigma``, use::
  
          sigma * np.matlib.randn(...) + mu
  
      Examples
      --------
      >>> np.random.seed(123)
      >>> import numpy.matlib
      >>> np.matlib.randn(1)
      matrix([[-1.0856306]])
      >>> np.matlib.randn(1, 2, 3)
      matrix([[ 0.99734545,  0.2829785 , -1.50629471],
              [-0.57860025,  1.65143654, -2.42667924]])
  
      Two-by-four matrix of samples from the normal distribution with
      mean 3 and standard deviation 2.5:
  
      >>> 2.5 * np.matlib.randn((2, 4)) + 3
      matrix([[1.92771843, 6.16484065, 0.83314899, 1.30278462],
              [2.76322758, 6.72847407, 1.40274501, 1.8900451 ]])
  
      """
      if isinstance(args[0], tuple):
          args = args[0]
      return asmatrix(np.random.randn(*args))
  
file_path: numpy/numpy/matlib.py
imports: import warnings
  import numpy
  from numpy.matrixlib.defmatrix import matrix, asmatrix
  from numpy import *
label_desc: Use image node_id 7 for calling a global function with example usage: randn() and returns: asmatrix