class_docstr: Hurdle LogNormal log-likelihood.
  
  .. math::
  
      f(x \mid \psi, \mu, \sigma) =
          \left\{
              \begin{array}{l}
              (1 - \psi)  \ \text{if } x = 0 \\
              \psi
              \frac{\text{LogNormalPDF}(x \mid \mu, \sigma))}
              {1 - \text{LogNormalCDF}(\epsilon \mid \mu, \sigma)} \ \text{if } x=1,2,3,\ldots
              \end{array}
          \right.
  
  where :math:`\epsilon` is the machine precision.
  
  Parameters
  ----------
  psi : tensor_like of float
      Expected proportion of LogNormal variates (0 < psi < 1)
  mu : tensor_like of float, default 0
      Location parameter.
  sigma : tensor_like of float, optional
      Standard deviation. (sigma > 0). (only required if tau is not specified).
      Defaults to 1.
  tau : tensor_like of float, optional
      Scale parameter (tau > 0). (only required if sigma is not specified).
      Defaults to 1.
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code: def __new__(cls, name, psi, mu=0, sigma=None, tau=None, **kwargs):
      return _hurdle_mixture(
          name=name,
          nonzero_p=psi,
          nonzero_dist=LogNormal.dist(mu=mu, sigma=sigma, tau=tau),
          dtype="float",
          **kwargs,
      )
  
imports: import warnings
  import numpy
  import pytensor
  import pytensor.tensor
  from pytensor.graph.basic import Node, equal_computations
  from pytensor.tensor import TensorVariable
  from pytensor.tensor.random.op import RandomVariable
  from pymc.distributions import transforms
  from pymc.distributions.continuous import Gamma, LogNormal, Normal, get_tau_sigma
  from pymc.distributions.discrete import Binomial, NegativeBinomial, Poisson
  from pymc.distributions.dist_math import check_parameters
  from pymc.distributions.distribution import DiracDelta, Distribution, SymbolicRandomVariable, _moment, moment
  from pymc.distributions.shape_utils import _change_dist_size, change_dist_size
  from pymc.distributions.transforms import _default_transform
  from pymc.distributions.truncated import Truncated
  from pymc.logprob.abstract import _logcdf, _logcdf_helper, _logprob
  from pymc.logprob.basic import logp
  from pymc.logprob.transforms import IntervalTransform
  from pymc.pytensorf import floatX
  from pymc.util import check_dist_not_registered
  from pymc.vartypes import continuous_types, discrete_types
label_desc: Use image node_id 1 for calling the HurdleLogNormal obj's underlying member method code with example usage: obj.__new__(cls, name, psi, mu, sigma, tau) and returns: _hurdle_mixture