class_docstr: A placeholder used to specify a log-likelihood for a mixture sub-graph.
class_docstr_tok: ["A","placeholder","used","to","specify","a","log-likelihood","for","a","mixture","sub-graph","."]
code: def update(self, node: Node):
      # Update for the internal mix_indexes RV
      return {node.inputs[0]: node.outputs[0]}
  
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 MarginalMixtureRV obj's underlying member method code with example usage: obj.update(node) and returns: dict