Fix some doctests and exclude import of version >=0.6.1 of xarray bec…

…ause of arviz-devs/arviz#1387

pymc4/distributions/multivariate.py

@@ -184,9 +184,10 @@ class MvNormal(ContinuousDistribution):
     matrix.
 
     >>> import numpy as np
+    >>> import pymc4 as pm
     >>> covariance_matrix = np.array([[1., 0.5], [0.5, 2]])
     >>> mu = np.zeros(2)
-    >>> vals = pm.MvNormal('vals', loc=loc, covariance_matrix=covariance_matrix, shape=(5, 2))
+    >>> vals = pm.MvNormal('vals', loc=mu, covariance_matrix=covariance_matrix, shape=(5, 2))
     """
 
     def __init__(self, name, loc, covariance_matrix, **kwargs):
@@ -362,10 +363,11 @@ class MvNormalCholesky(ContinuousDistribution):
     factor of the full covariance matrix (scale_tril).
 
     >>> import numpy as np
+    >>> import pymc4 as pm
     >>> covariance_matrix = np.array([[1., 0.5], [0.5, 2]])
     >>> chol_factor = np.linalg.cholesky(covariance_matrix)
     >>> mu = np.zeros(2)
-    >>> vals = pm.MvNormalCholesky('vals', loc=loc, scale_tril=chol_factor)
+    >>> vals = pm.MvNormalCholesky('vals', loc=mu, scale_tril=chol_factor)
     """
 
     def __init__(self, name, loc, scale_tril, **kwargs):

pymc4/inference/sampling.py

@@ -126,13 +126,14 @@ def sample(
     Nothing special is required but passing the model to ``pm.sample``, the rest configuration is
     held by PyMC4.
     >>> trace = sample(conditioned)
+
     Notes
     -----
     Things that are considered to be under discussion are overriding observed variables. The API
     for that may look like
     >>> new_observed = {"nested_model/n": np.random.randn(10) + 1}
     >>> trace = sample(conditioned, observed=new_observed)
-    This will give a trace with new observed variables. This way is considered to be explicit.
+    >>> # This will give a trace with new observed variables. This way is considered to be explicit.
     """
     # assign sampler is no sampler_type is passed``
     sampler_assigned: str = auto_assign_sampler(model, sampler_type)