code: def beta(a, b, size=None, dtype=None, device=None):
      r"""Draw samples from a Beta distribution.
  
      The Beta distribution is a special case of the Dirichlet distribution,
      and is related to the Gamma distribution.  It has the probability
      distribution function
  
      .. math:: f(x; a,b) = \frac{1}{B(\alpha, \beta)} x^{\alpha - 1}
                                                       (1 - x)^{\beta - 1},
  
      where the normalisation, B, is the beta function,
  
      .. math:: B(\alpha, \beta) = \int_0^1 t^{\alpha - 1}
                                   (1 - t)^{\beta - 1} dt.
  
      It is often seen in Bayesian inference and order statistics.
  
      Parameters
      ----------
      a : float or array_like of floats
          Alpha, positive (>0).
      b : float or array_like of floats
          Beta, positive (>0).
      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.  If size is ``None`` (default),
          a single value is returned if ``a`` and ``b`` are both scalars.
          Otherwise, ``np.broadcast(a, b).size`` samples are drawn.
      dtype : {'float16', 'float32', 'float64'}, optional
          Data type of output samples. Default is 'float32'.
          Dtype 'float32' or 'float64' is strongly recommended,
          since lower precision might lead to out of range issue.
      device : Device, optional
          Device context of output. Default is current device.
  
      Notes
      -----
      To use this operator with scalars as input, please run
      ``npx.set_np()`` first.
  
      Returns
      -------
      out : ndarray or scalar
          Drawn samples from the parameterized beta distribution.
      """
      return _mx_nd_np.random.beta(
          a, b, size=size, dtype=dtype, device=device
      )
  
imports: from ..ndarray import numpy
  from ..random import seed
  from ..util import wrap_ctx_to_device_func
label_desc: Use image node_id 18 for calling a global function with example usage: beta(a, b, size, dtype, device) and returns: _mx_nd_np