BUG: fix Pymc sampler import

examples/core_examples/alternative_samplers/linear_regression_pymc_custom_likelihood.py

@@ -12,6 +12,7 @@ import bilby
 import matplotlib.pyplot as plt
 import numpy as np
 import pymc as pm
+from bilby.core.sampler.pymc import Pymc
 
 # A few simple setup steps
 label = "linear_regression_pymc_custom_likelihood"
@@ -76,13 +77,11 @@ class GaussianLikelihoodPyMC(bilby.core.likelihood.GaussianLikelihood):
         ----------
         sampler: :class:`bilby.core.sampler.Pymc`
             A Sampler object must be passed containing the prior distributions
-            and PyMC3 :class:`~pymc3.Model` to use as a context manager.
+            and PyMC :class:`~pymc.Model` to use as a context manager.
             If this is not passed, the super class is called and the regular
             likelihood is evaluated.
         """
 
-        from bilby.core.sampler.pymc import Pymc
-
         if not isinstance(sampler, Pymc):
             return super(GaussianLikelihoodPyMC, self).log_likelihood()
 
@@ -116,15 +115,12 @@ class PyMCUniformPrior(bilby.core.prior.Uniform):
 
     def ln_prob(self, sampler=None):
         """
-        Change ln_prob method to take in a Sampler and return a PyMC3
+        Change ln_prob method to take in a Sampler and return a PyMC
         distribution.
 
-        If the passed argument is not a `Pymc3` sampler, assume that it is a
+        If the passed argument is not a `Pymc` sampler, assume that it is a
         float or array to be passed to the superclass.
         """
-
-        from bilby.core.sampler import Pymc
-
         if not isinstance(sampler, Pymc):
             return super(PyMCUniformPrior, self).ln_prob(sampler)