class_docstr: A Poisson subsampled Gaussian privacy random variable.
  
  For details about the formulas for the pdf and cdf, see propositions B1 and B4 in
  https://www.microsoft.com/en-us/research/publication/numerical-composition-of-differential-privacy/
code: def pdf(self, t):
      q = self.sample_rate
      sigma = self.noise_multiplier
  
      z = np.log((np.exp(t) + q - 1) / q)
  
      return np.where(
          t > np.log(1 - q),
          sigma
          * np.exp(
              -(sigma**2) * z**2 / 2 - 1 / (8 * sigma**2) + 2 * t
          )
          / (
              SQRT2
              * np.sqrt(np.pi)
              * (np.exp(t) + q - 1)
              * ((np.exp(t) + q - 1) / q) ** 0.5
          ),
          0.0,
      )
  
imports: from dataclasses import dataclass
  from typing import Tuple
  import numpy
  from scipy import integrate
  from scipy.special import erfc
  from ..rdp import _compute_rdp
  from .domain import Domain
label_desc: Use image node_id 2 for calling the PoissonSubsampledGaussianPRV obj's underlying member method code with example usage: obj.pdf(t) and returns: np