code: def standard_cauchy(
      random_state, size=None, chunk_size=None, gpu=None, dtype=None
  ):
      r"""
      Draw samples from a standard Cauchy distribution with mode = 0.
  
      Also known as the Lorentz distribution.
  
      Parameters
      ----------
      size : int or tuple of ints, optional
          Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
          ``m * n * k`` samples are drawn.  Default is None, in which case a
          single value is returned.
      chunk_size : int or tuple of int or tuple of ints, optional
          Desired chunk size on each dimension
      gpu : bool, optional
          Allocate the tensor on GPU if True, False as default
      dtype : data-type, optional
        Data-type of the returned tensor.
  
      Returns
      -------
      samples : Tensor or scalar
          The drawn samples.
  
      Notes
      -----
      The probability density function for the full Cauchy distribution is
  
      .. math:: P(x; x_0, \gamma) = \frac{1}{\pi \gamma \bigl[ 1+
                (\frac{x-x_0}{\gamma})^2 \bigr] }
  
      and the Standard Cauchy distribution just sets :math:`x_0=0` and
      :math:`\gamma=1`
  
      The Cauchy distribution arises in the solution to the driven harmonic
      oscillator problem, and also describes spectral line broadening. It
      also describes the distribution of values at which a line tilted at
      a random angle will cut the x axis.
  
      When studying hypothesis tests that assume normality, seeing how the
      tests perform on data from a Cauchy distribution is a good indicator of
      their sensitivity to a heavy-tailed distribution, since the Cauchy looks
      very much like a Gaussian distribution, but with heavier tails.
  
      References
      ----------
      .. [1] NIST/SEMATECH e-Handbook of Statistical Methods, "Cauchy
            Distribution",
            http://www.itl.nist.gov/div898/handbook/eda/section3/eda3663.htm
      .. [2] Weisstein, Eric W. "Cauchy Distribution." From MathWorld--A
            Wolfram Web Resource.
            http://mathworld.wolfram.com/CauchyDistribution.html
      .. [3] Wikipedia, "Cauchy distribution"
            http://en.wikipedia.org/wiki/Cauchy_distribution
  
      Examples
      --------
      Draw samples and plot the distribution:
  
      >>> import mars.tensor as mt
      >>> import matplotlib.pyplot as plt
  
      >>> s = mt.random.standard_cauchy(1000000)
      >>> s = s[(s>-25) & (s<25)]  # truncate distribution so it plots well
      >>> plt.hist(s.execute(), bins=100)
      >>> plt.show()
      """
      if dtype is None:
          dtype = (
              np.random.RandomState().standard_cauchy(size=(0,)).dtype
          )
      size = random_state._handle_size(size)
      seed = gen_random_seeds(1, random_state.to_numpy())[0]
      op = TensorStandardCauchy(
          size=size, seed=seed, gpu=gpu, dtype=dtype
      )
      return op(chunk_size=chunk_size)
  
file_path: mars/mars/tensor/random/standard_cauchy.py
imports: import numpy
  from ...None import opcodes
  from ..utils import gen_random_seeds
  from .core import TensorRandomOperandMixin, TensorDistribution
label_desc: Use image node_id 1 for calling a global function with example usage: standard_cauchy(random_state, size, chunk_size, gpu, dtype) and returns: op