diff --git a/src/pytorch_tabular/config/config.py b/src/pytorch_tabular/config/config.py
index 999c2c4a..ce6e1d0d 100644
--- a/src/pytorch_tabular/config/config.py
+++ b/src/pytorch_tabular/config/config.py
@@ -192,9 +192,9 @@ class DataConfig:
     )
 
     def __post_init__(self):
-        assert (
-            len(self.categorical_cols) + len(self.continuous_cols) + len(self.date_columns) > 0
-        ), "There should be at-least one feature defined in categorical, continuous, or date columns"
+        assert len(self.categorical_cols) + len(self.continuous_cols) + len(self.date_columns) > 0, (
+            "There should be at-least one feature defined in categorical, continuous, or date columns"
+        )
         _validate_choices(self)
         if os.name == "nt" and self.num_workers != 0:
             print("Windows does not support num_workers > 0. Setting num_workers to 0")
@@ -255,9 +255,9 @@ class InferredConfig:
 
     def __post_init__(self):
         if self.embedding_dims is not None:
-            assert all(
-                (isinstance(t, Iterable) and len(t) == 2) for t in self.embedding_dims
-            ), "embedding_dims must be a list of tuples (cardinality, embedding_dim)"
+            assert all((isinstance(t, Iterable) and len(t) == 2) for t in self.embedding_dims), (
+                "embedding_dims must be a list of tuples (cardinality, embedding_dim)"
+            )
             self.embedded_cat_dim = sum([t[1] for t in self.embedding_dims])
         else:
             self.embedded_cat_dim = 0
@@ -287,8 +287,8 @@ class TrainerConfig:
                 'cpu','gpu','tpu','ipu', 'mps', 'auto'. Defaults to 'auto'.
                 Choices are: [`cpu`,`gpu`,`tpu`,`ipu`,'mps',`auto`].
 
-        devices (Optional[int]): Number of devices to train on (int). -1 uses all available devices. By
-                default, uses all available devices (-1)
+        devices (Union[int, List[int]]): Number of devices to train on (int), or list of device indices.
+                -1 uses all available devices. By default, uses all available devices (-1)
 
         devices_list (Optional[List[int]]): List of devices to train on (list). If specified, takes
                 precedence over `devices` argument. Defaults to None
@@ -400,7 +400,7 @@ class TrainerConfig:
             "choices": ["cpu", "gpu", "tpu", "ipu", "mps", "auto"],
         },
     )
-    devices: Optional[int] = field(
+    devices: Any = field(
         default=-1,
         metadata={
             "help": "Number of devices to train on. -1 uses all available devices."
diff --git a/tests/test_config.py b/tests/test_config.py
new file mode 100644
index 00000000..a59dab16
--- /dev/null
+++ b/tests/test_config.py
@@ -0,0 +1,73 @@
+#!/usr/bin/env python
+"""Tests for config classes."""
+
+from omegaconf import OmegaConf
+
+from pytorch_tabular.config import TrainerConfig
+
+
+class TestTrainerConfig:
+    """Tests for TrainerConfig class."""
+
+    def test_devices_list_to_devices_conversion(self):
+        """Test that devices_list is properly converted to devices."""
+        # Test with a list of devices
+        trainer_config = TrainerConfig(devices_list=[0, 1])
+        assert trainer_config.devices == [0, 1]
+
+        # Wrap with OmegaConf as done in TabularModel
+        config = OmegaConf.structured(trainer_config)
+        assert config.devices == [0, 1]
+
+    def test_devices_list_multiple_gpus(self):
+        """Test devices_list with multiple GPU IDs as documented."""
+        trainer_config = TrainerConfig(devices_list=[1, 2, 3, 4])
+        assert trainer_config.devices == [1, 2, 3, 4]
+
+        config = OmegaConf.structured(trainer_config)
+        assert config.devices == [1, 2, 3, 4]
+
+    def test_devices_int_value(self):
+        """Test that devices accepts integer values."""
+        trainer_config = TrainerConfig(devices=2)
+        assert trainer_config.devices == 2
+
+        config = OmegaConf.structured(trainer_config)
+        assert config.devices == 2
+
+    def test_devices_default_value(self):
+        """Test that devices has default value of -1."""
+        trainer_config = TrainerConfig()
+        assert trainer_config.devices == -1
+
+        config = OmegaConf.structured(trainer_config)
+        assert config.devices == -1
+
+    def test_devices_list_single_device(self):
+        """Test devices_list with a single device."""
+        trainer_config = TrainerConfig(devices_list=[0])
+        assert trainer_config.devices == [0]
+
+        config = OmegaConf.structured(trainer_config)
+        assert config.devices == [0]
+
+    def test_devices_list_precedence(self):
+        """Test that devices_list takes precedence over devices."""
+        # When both are provided, devices_list should take precedence
+        trainer_config = TrainerConfig(devices=2, devices_list=[0, 1])
+        assert trainer_config.devices == [0, 1]
+
+        config = OmegaConf.structured(trainer_config)
+        assert config.devices == [0, 1]
+
+    def test_omegaconf_merge_compatibility(self):
+        """Test that config works correctly with OmegaConf.merge."""
+        trainer_config = TrainerConfig(devices_list=[0, 1], max_epochs=10)
+        config = OmegaConf.structured(trainer_config)
+
+        # Simulate merging as done in TabularModel
+        merged = OmegaConf.merge(OmegaConf.to_container(config), {"accelerator": "gpu"})
+
+        assert merged.devices == [0, 1]
+        assert merged.max_epochs == 10
+        assert merged.accelerator == "gpu"