diff --git a/pymc3/distributions/distribution.py b/pymc3/distributions/distribution.py
index 582fa6b2f8..e72a90bed1 100644
--- a/pymc3/distributions/distribution.py
+++ b/pymc3/distributions/distribution.py
@@ -339,17 +339,15 @@ def dist(
 
 @singledispatch
 def _get_moment(op, rv, size, *rv_inputs) -> TensorVariable:
-    """Fallback method for creating an initial value for a random variable.
-
-    Parameters are the same as for the `.dist()` method.
-    """
     return None
 
 
 def get_moment(rv: TensorVariable) -> TensorVariable:
-    """Fallback method for creating an initial value for a random variable.
+    """Method for choosing a representative point/value
+    that can be used to start optimization or MCMC sampling.
 
-    Parameters are the same as for the `.dist()` method.
+    The only parameter to this function is the RandomVariable
+    for which the value is to be derived.
     """
     size = rv.owner.inputs[1]
     return _get_moment(rv.owner.op, rv, size, *rv.owner.inputs[3:])
diff --git a/pymc3/sampling.py b/pymc3/sampling.py
index 8bfd06a1ee..f032ad1fda 100644
--- a/pymc3/sampling.py
+++ b/pymc3/sampling.py
@@ -31,7 +31,6 @@
 import xarray
 
 from aesara.compile.mode import Mode
-from aesara.graph.basic import Constant
 from aesara.tensor.sharedvar import SharedVariable
 from arviz import InferenceData
 from fastprogress.fastprogress import progress_bar
@@ -2002,7 +2001,7 @@ def sample_prior_predictive(
             names.append(rv_var.name)
             vars_to_sample.append(rv_var)
 
-    inputs = [i for i in inputvars(vars_to_sample) if not isinstance(i, (SharedVariable, Constant))]
+    inputs = [i for i in inputvars(vars_to_sample) if not isinstance(i, SharedVariable)]
 
     sampler_fn = compile_rv_inplace(
         inputs, vars_to_sample, allow_input_downcast=True, accept_inplace=True, mode=mode