code: def simple_model():
      seconds = np.linspace(0, 5)
      observations = np.random.normal(
          0.5 + np.random.uniform(size=3)[:, None] * seconds[None, :]
      )
      with pm.Model(
          coords={
              "condition": ["A", "B", "C"],
          }
      ) as pmodel:
          x = pm.ConstantData("seconds", seconds, dims="time")
          a = pm.Normal("scalar")
          b = pm.Uniform("vector", dims="condition")
          pm.Deterministic(
              "matrix",
              a + b[:, None] * x[None, :],
              dims=("condition", "time"),
          )
          pm.Bernoulli("integer", p=0.5)
          obs = pm.MutableData(
              "obs", observations, dims=("condition", "time")
          )
          pm.Normal(
              "L",
              pmodel["matrix"],
              observed=obs,
              dims=("condition", "time"),
          )
      return pmodel
  
imports: import logging
  import arviz
  import numpy
  import pytest
  import pymc
  from pymc.backends import init_traces
  from pymc.step_methods.arraystep import ArrayStepShared
  from pymc.backends.mcbackend import ChainRecordAdapter, find_data, get_variables_and_point_fn, make_runmeta_and_point_fn
label_desc: Use image node_id 1 for calling a global function with example usage: simple_model() and returns: pmodel