Enhance data parsing and validation, add extra info types files

- Improved `parse_int_list` and `parse_float_list` functions to support JSON list input.
- Introduced `validate_dataset_metadata` function to ensure dataset metadata consistency with training configuration.
- Added multiple new files for extra information types, categorizing them into assessment-only, exposure-only, and combined types.
- Removed deprecated `merge_extra_info_types` function and adjusted related logic in `train.py`.
- Updated `save_config` function to accept additional metadata for training runs.
- Refactored model and training scripts for better clarity and maintainability.

models.py

@@ -94,7 +94,7 @@ class DeepHealth(nn.Module):
             ])
             self.rope = None
             self.rbf = None
-        if time_mode == "relative":
+        elif time_mode == "relative":
             self.age_encoding = None
             self.blocks = nn.ModuleList([
                 GPTBlock(
@@ -154,6 +154,9 @@ class DeepHealth(nn.Module):
         other_time: torch.FloatTensor | None = None,
         **unused_kwargs,
     ) -> torch.Tensor:
+        if unused_kwargs:
+            unknown = ", ".join(sorted(unused_kwargs))
+            raise TypeError(f"Unexpected DeepHealth forward arguments: {unknown}")
         if mode not in {"next_token", "all_future"}:
             raise ValueError("mode must be either 'next_token' or 'all_future'")
         if mode == "all_future" and t_query is None: