code: def test_model_latex_repr_mixture_model():
      with Model() as mix_model:
          w = Dirichlet("w", [1, 1])
          mix = Mixture(
              "mix",
              w=w,
              comp_dists=[Normal.dist(0.0, 5.0), StudentT.dist(7.0)],
          )
  
      latex_repr = mix_model.str_repr(formatting="latex")
      expected = [
          "$$",
          "\\begin{array}{rcl}",
          "\\text{w} &\\sim & "
          "\\operatorname{Dirichlet}(\\text{<constant>})\\\\\\text{mix} &\\sim & "
          "\\operatorname{MarginalMixture}(\\text{w},~\\operatorname{Normal}(0,~5),~\\operatorname{StudentT}(7,~0,~1))",
          "\\end{array}",
          "$$",
      ]
      assert [
          line.strip() for line in latex_repr.split("\n")
      ] == expected
  
file_path: pymc/tests/test_printing.py
imports: import numpy
  from pytensor.tensor.random import normal
  from pymc import Bernoulli, Censored, HalfCauchy, Mixture, StudentT
  from pymc.distributions import Dirichlet, DirichletMultinomial, HalfNormal, KroneckerNormal, MvNormal, NegativeBinomial, Normal, Uniform, ZeroInflatedPoisson
  from pymc.math import dot
  from pymc.model import Deterministic, Model, Potential
  from pymc.pytensorf import floatX
label_desc: Use image node_id 2 for calling a global function with example usage: test_model_latex_repr_mixture_model() without return types