class_docstr: A univariate random variable.
class_docstr_tok: ["A","univariate","random","variable","."]
docstr: Evaluate the probability density function at x.
  
  Parameters
  ----------
  x : array-like, shape=[n_samples, *support_shape]
      Sample points at which to compute the probability density function.
  point : array-like, shape=[..., *space.shape]
      Point representing a distribution.
  
  Returns
  -------
  pdf_at_x : array-like, shape=[..., n_samples]
      Values of pdf at x for each value of the parameters provided
      by point.
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code: def pdf(self, sample, point):
      """Evaluate the probability density function at x.
  
      Parameters
      ----------
      x : array-like, shape=[n_samples, *support_shape]
          Sample points at which to compute the probability density function.
      point : array-like, shape=[..., *space.shape]
          Point representing a distribution.
  
      Returns
      -------
      pdf_at_x : array-like, shape=[..., n_samples]
          Values of pdf at x for each value of the parameters provided
          by point.
      """
      batch_shape = get_batch_shape(self.space.point_ndim, point)
      n_points = math.prod(batch_shape)
      n_samples = sample.shape[0]
  
      pre_flat_shape = batch_shape + (n_samples,)
      params = self._flatten_params(point, pre_flat_shape)
  
      sample_ = gs.reshape(
          gs.broadcast_to(sample, (n_points, n_samples)), (-1,)
      )
  
      pdf = gs.from_numpy(self.scp_pdf(sample_, **params))
  
      expected_shape = (
          batch_shape + (n_samples,) + self.space.support_shape
      )
      return self._unflatten_res(pdf, expected_shape)
  
imports: import math
  import geomstats.backend
  from geomstats.vectorization import get_batch_shape
label_desc: Use image node_id 3 for calling the ScipyUnivariateRandomVariable obj's underlying member method code with example usage: obj.pdf(sample, point) and returns: self