docstr: Create a frozen multivariate normal distribution.
  
  Parameters
  ----------
  mean : array_like, optional
      Mean of the distribution (default zero)
  cov : array_like, optional
      Covariance matrix of the distribution (default one)
  allow_singular : bool, optional
      If this flag is True then tolerate a singular
      covariance matrix (default False).
  seed : {None, int, `~np.random.RandomState`, `~np.random.Generator`}, optional
      This parameter defines the object to use for drawing random
      variates.
      If `seed` is `None` the `~np.random.RandomState` singleton is used.
      If `seed` is an int, a new ``RandomState`` instance is used, seeded
      with seed.
      If `seed` is already a ``RandomState`` or ``Generator`` instance,
      then that object is used.
      Default is None.
  maxpts: integer, optional
      The maximum number of points to use for integration of the
      cumulative distribution function (default `1000000*dim`)
  abseps: float, optional
      Absolute error tolerance for the cumulative distribution function
      (default 1e-5)
  releps: float, optional
      Relative error tolerance for the cumulative distribution function
      (default 1e-5)
  
  Examples
  --------
  When called with the default parameters, this will create a 1D random
  variable with mean 0 and covariance 1:
  
  >>> from scipy.stats import multivariate_normal
  >>> r = multivariate_normal()
  >>> r.mean
  array([ 0.])
  >>> r.cov
  array([[1.]])
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code: def __init__(
      self,
      mean=None,
      cov=1,
      allow_singular=False,
      seed=None,
      maxpts=None,
      abseps=1e-5,
      releps=1e-5,
  ):
      """
      Create a frozen multivariate normal distribution.
  
      Parameters
      ----------
      mean : array_like, optional
          Mean of the distribution (default zero)
      cov : array_like, optional
          Covariance matrix of the distribution (default one)
      allow_singular : bool, optional
          If this flag is True then tolerate a singular
          covariance matrix (default False).
      seed : {None, int, `~np.random.RandomState`, `~np.random.Generator`}, optional
          This parameter defines the object to use for drawing random
          variates.
          If `seed` is `None` the `~np.random.RandomState` singleton is used.
          If `seed` is an int, a new ``RandomState`` instance is used, seeded
          with seed.
          If `seed` is already a ``RandomState`` or ``Generator`` instance,
          then that object is used.
          Default is None.
      maxpts: integer, optional
          The maximum number of points to use for integration of the
          cumulative distribution function (default `1000000*dim`)
      abseps: float, optional
          Absolute error tolerance for the cumulative distribution function
          (default 1e-5)
      releps: float, optional
          Relative error tolerance for the cumulative distribution function
          (default 1e-5)
  
      Examples
      --------
      When called with the default parameters, this will create a 1D random
      variable with mean 0 and covariance 1:
  
      >>> from scipy.stats import multivariate_normal
      >>> r = multivariate_normal()
      >>> r.mean
      array([ 0.])
      >>> r.cov
      array([[1.]])
  
      """
      self._dist = multivariate_normal_gen(seed)
      self.dim, self.mean, self.cov = self._dist._process_parameters(
          None, mean, cov
      )
      self.cov_info = _PSD(self.cov, allow_singular=allow_singular)
      if not maxpts:
          maxpts = 1000000 * self.dim
      self.maxpts = maxpts
      self.abseps = abseps
      self.releps = releps
  
imports: import numpy
  import scipy.linalg
  from scipy._lib import doccer
  from scipy.special import gammaln
  from scipy._lib._util import check_random_state
  from scipy.stats import mvn
label_desc: Use image node_id 1 to create a new multivariate_normal_frozen object from inherited base classes: multi_rv_frozen with example: obj = multivariate_normal_frozen(mean, cov, allow_singular, seed, maxpts, abseps, releps)