fix for pytorch 1.5 and pydantic

pts/core/component.py

@@ -95,26 +95,22 @@ def validated(base_model=None):
                 param.annotation
                 if param.annotation != inspect.Parameter.empty
                 else Any,
-                param.default
-                if param.default != inspect.Parameter.empty
-                else ...,
+                param.default if param.default != inspect.Parameter.empty else ...,
             )
             for param in init_params.values()
             if param.name != "self"
-               and param.kind == inspect.Parameter.POSITIONAL_OR_KEYWORD
+            and param.kind == inspect.Parameter.POSITIONAL_OR_KEYWORD
         }
 
         if base_model is None:
             PydanticModel = create_model(
-                model_name=f"{init_clsnme}Model",
+                f"{init_clsnme}Model",
                 __config__=BaseValidatedInitializerModel.Config,
                 **init_fields,
             )
         else:
             PydanticModel = create_model(
-                model_name=f"{init_clsnme}Model",
-                __base__=base_model,
-                **init_fields,
+                f"{init_clsnme}Model", __base__=base_model, **init_fields,
             )
 
         def validated_repr(self) -> str:
@@ -129,9 +125,7 @@ def validated(base_model=None):
 
             nmargs = {
                 name: arg
-                for (name, param), arg in zip(
-                    list(init_params.items()), [self] + args
-                )
+                for (name, param), arg in zip(list(init_params.items()), [self] + args)
                 if name != "self"
             }
             model = PydanticModel(**{**nmargs, **kwargs})
@@ -160,4 +154,4 @@ def validated(base_model=None):
 
         return init_wrapper
 
-    return validator
+    return validator

test/modules/test_distribution_output.py

@@ -230,9 +230,9 @@ def test_lowrank_multivariate_normal() -> None:
     Sigma = cov_factor @ cov_factor.T + cov_diag
 
     distr = LowRankMultivariateNormal(
-        loc=torch.Tensor(loc),
-        cov_diag=torch.Tensor(np.diag(cov_diag)),
-        cov_factor=torch.Tensor(cov_factor),
+        loc=torch.Tensor(loc.copy()),
+        cov_diag=torch.Tensor(np.diag(cov_diag).copy()),
+        cov_factor=torch.Tensor(cov_factor.copy()),
     )
 
     assert np.allclose(