Add return type hints

pymc/distributions/logprob.py

@@ -39,7 +39,9 @@
 from pymc.aesaraf import floatX
 
 
-def _get_scaling(total_size: Optional[Union[int, Sequence[int]]], shape, ndim: int):
+def _get_scaling(
+    total_size: Optional[Union[int, Sequence[int]]], shape, ndim: int
+) -> TensorVariable:
     """
     Gets scaling constant for logp.
 
@@ -288,14 +290,14 @@ def logp(rv: TensorVariable, value) -> TensorVariable:
             raise NotImplementedError("PyMC could not infer logp of input variable.") from exc
 
 
-def logcdf(rv, value):
+def logcdf(rv, value) -> TensorVariable:
     """Return the log-cdf graph of a Random Variable"""
 
     value = at.as_tensor_variable(value, dtype=rv.dtype)
     return logcdf_aeppl(rv, value)
 
 
-def ignore_logprob(rv):
+def ignore_logprob(rv) -> TensorVariable:
     """Return a duplicated variable that is ignored when creating Aeppl logprob graphs
 
     This is used in SymbolicDistributions that use other RVs as inputs but account