diff --git a/pyrad/engine/trainer.py b/pyrad/engine/trainer.py
index eca8d307ec..b8fdbe9783 100644
--- a/pyrad/engine/trainer.py
+++ b/pyrad/engine/trainer.py
@@ -15,6 +15,7 @@
 """
 Code to train model.
 """
+import functools
 import logging
 import os
 from typing import Dict
@@ -82,15 +83,14 @@ def setup(self, test_mode=False):
         self.graph.register_callbacks()
 
     @classmethod
-    def get_aggregated_loss(cls, loss_dict: Dict[str, torch.tensor]):
+    def get_aggregated_loss(cls, loss_dict: Dict[str, torch.Tensor]) -> torch.Tensor:
         """Returns the aggregated losses and the scalar for calling .backwards() on.
         # TODO: move this out to another file/class/etc.
         """
-        loss_sum = 0.0
-        for loss_name in loss_dict.keys():
-            # TODO(ethan): add loss weightings here from a config
-            loss_sum += loss_dict[loss_name]
-        return loss_sum
+        # TODO(ethan): add loss weightings here from a config
+        # e.g. weighted_losses = map(lambda k: some_weight_dict[k] * loss_dict[k], loss_dict.keys())
+        weighted_losses = loss_dict.values()
+        return functools.reduce(torch.add, weighted_losses)
 
     def train(self) -> None:
         """Train the model."""
diff --git a/pyrad/fields/instant_ngp_field.py b/pyrad/fields/instant_ngp_field.py
index 9870444e35..12bc0bfc9f 100644
--- a/pyrad/fields/instant_ngp_field.py
+++ b/pyrad/fields/instant_ngp_field.py
@@ -31,7 +31,7 @@
 
 try:
     import tinycudann as tcnn
-except ImportError as e:
+except ImportError:
     # tinycudann module doesn't exist
     pass
 
diff --git a/pyrad/graphs/base.py b/pyrad/graphs/base.py
index d65a977edc..c1c9cf05c4 100644
--- a/pyrad/graphs/base.py
+++ b/pyrad/graphs/base.py
@@ -140,7 +140,7 @@ def forward_after_ray_generator(self, ray_bundle: RayBundle, batch: Union[str, D
         """Run forward starting with a ray bundle."""
         intersected_ray_bundle = self.collider(ray_bundle)
 
-        if isinstance(batch, type(None)):
+        if batch is None:
             # during inference, keep all rays
             outputs = self.get_outputs(intersected_ray_bundle)
             return outputs
diff --git a/pyrad/utils/config.py b/pyrad/utils/config.py
index 8b4fa1afb5..f6ff416768 100644
--- a/pyrad/utils/config.py
+++ b/pyrad/utils/config.py
@@ -14,9 +14,9 @@
 
 """Structured config classes"""
 
-from dataclasses import MISSING, dataclass
+from dataclasses import dataclass
 from typing import Any, Dict, Optional
-from omegaconf import DictConfig
+from omegaconf import DictConfig, MISSING
 
 
 @dataclass
@@ -56,8 +56,7 @@ class TrainerConfig:
     steps_per_save: int = MISSING
     steps_per_test: int = MISSING
     max_num_iterations: int = MISSING
-    # additional optional parameters here
-    resume_train: Optional[ResumeTrainConfig] = None
+    resume_train: ResumeTrainConfig = MISSING
 
 
 @dataclass
diff --git a/pyrad/utils/profiler.py b/pyrad/utils/profiler.py
index fc409a82c7..ee2633a0c8 100644
--- a/pyrad/utils/profiler.py
+++ b/pyrad/utils/profiler.py
@@ -77,7 +77,11 @@ def update_time(self, func_name: str, start_time: float, end_time: float):
     def print_profile(self):
         """helper to print out the profiler stats"""
         logging.info("Printing profiling stats, from longest to shortest duration in seconds")
-        sorted_keys = [k for k, _ in sorted(self.profiler_dict.items(), key=lambda item: item[1]["val"], reverse=True)]
+        sorted_keys = sorted(
+            self.profiler_dict.keys(),
+            key=lambda k: self.profiler_dict[k]["val"],
+            reverse=True,
+        )
         for k in sorted_keys:
             val = f"{self.profiler_dict[k]['val']:0.4f}"
             print(f"{k:<20}: {val:<20}")
diff --git a/scripts/run_train.py b/scripts/run_train.py
index 5d9d3c60c4..b674c2ada6 100644
--- a/scripts/run_train.py
+++ b/scripts/run_train.py
@@ -2,7 +2,6 @@
 run_train_nerf.py
 """
 
-import datetime
 import logging
 import os
 import random
@@ -56,7 +55,7 @@ def _distributed_worker(
     machine_rank: int,
     dist_url: str,
     config: Config,
-    timeout: datetime = DEFAULT_TIMEOUT,
+    timeout: timedelta = DEFAULT_TIMEOUT,
 ) -> Any:
     """Spawned distributed worker that handles the initialization of process group and handles the
        training process on multiple processes
@@ -70,8 +69,8 @@ def _distributed_worker(
         dist_url (str): url to connect to for distributed jobs, including protocol
                         e.g. "tcp://127.0.0.1:8686".
                         Can be set to "auto" to automatically select a free port on localhost
-        config (Config): config file specifying trainng regimen
-        timeout (datetime, optional): timeout of the distributed workers Defaults to DEFAULT_TIMEOUT.
+        config (Config): config file specifying training regimen
+        timeout (timedelta, optional): timeout of the distributed workers Defaults to DEFAULT_TIMEOUT.
 
     Raises:
         e: Exception in initializing the process group
@@ -137,7 +136,7 @@ def launch(
     machine_rank: int = 0,
     dist_url: str = "auto",
     config: Config = None,
-    timeout: datetime = DEFAULT_TIMEOUT,
+    timeout: timedelta = DEFAULT_TIMEOUT,
 ) -> None:
     """Function that spawns muliple processes to call on main_func
 
@@ -147,8 +146,8 @@ def launch(
         num_machines (int, optional): total number of machines
         machine_rank (int, optional): rank of this machine. Defaults to 0.
         dist_url (str, optional): url to connect to for distributed jobs. Defaults to "auto".
-        config (Config, optional): config file specifying trainng regimen Defaults to None.
-        timeout (datetime, optional): timeout of the distributed workers Defaults to DEFAULT_TIMEOUT.
+        config (Config, optional): config file specifying training regimen. Defaults to None.
+        timeout (timedelta, optional): timeout of the distributed workers. Defaults to DEFAULT_TIMEOUT.
     """
     world_size = num_machines * num_gpus_per_machine
     if world_size == 0: