code: def randn(random_state, *dn, **kw):
      r"""
      Return a sample (or samples) from the "standard normal" distribution.
  
      If positive, int_like or int-convertible arguments are provided,
      `randn` generates an array of shape ``(d0, d1, ..., dn)``, filled
      with random floats sampled from a univariate "normal" (Gaussian)
      distribution of mean 0 and variance 1 (if any of the :math:`d_i` are
      floats, they are first converted to integers by truncation). A single
      float randomly sampled from the distribution is returned if no
      argument is provided.
  
      This is a convenience function.  If you want an interface that takes a
      tuple as the first argument, use `numpy.random.standard_normal` instead.
  
      Parameters
      ----------
      d0, d1, ..., dn : int, optional
          The dimensions of the returned tensor, should be all positive.
          If no argument is given a single Python float is returned.
  
      Returns
      -------
      Z : Tensor or float
          A ``(d0, d1, ..., dn)``-shaped array of floating-point samples from
          the standard normal distribution, or a single such float if
          no parameters were supplied.
  
      See Also
      --------
      random.standard_normal : Similar, but takes a tuple as its argument.
  
      Notes
      -----
      For random samples from :math:`N(\mu, \sigma^2)`, use:
  
      ``sigma * mt.random.randn(...) + mu``
  
      Examples
      --------
      >>> import mars.tensor as mt
  
      >>> mt.random.randn().execute()
      2.1923875335537315 #random
  
      Two-by-four tensor of samples from N(3, 6.25):
  
      >>> (2.5 * mt.random.randn(2, 4) + 3).execute()
      array([[-4.49401501,  4.00950034, -1.81814867,  7.29718677],  #random
             [ 0.39924804,  4.68456316,  4.99394529,  4.84057254]]) #random
      """
      if len(dn) == 1 and isinstance(dn[0], (tuple, list)):
          raise TypeError(
              "'tuple' object cannot be interpreted as an integer"
          )
      if "dtype" not in kw:
          kw["dtype"] = np.dtype("f8")
      chunk_size = kw.pop("chunk_size", None)
  
      seed = gen_random_seeds(1, random_state.to_numpy())[0]
      op = TensorRandn(seed=seed, size=dn, **kw)
  
      for key in op.extra_params:
          if not key.startswith("_"):
              raise ValueError(
                  f"randn got unexpected key arguments {key}"
              )
  
      return op(chunk_size=chunk_size)
  
file_path: mars/mars/tensor/random/randn.py
imports: import numpy
  from ...None import opcodes
  from ..utils import gen_random_seeds
  from .core import TensorRandomOperandMixin, TensorSimpleRandomData
label_desc: Use image node_id 1 for calling a global function with example usage: randn(random_state) and returns: op