Merge branch 'materialize_train_test' of https://github.com/HoustonJ2…

…013/pytorch-frame into materialize_train_test

README.md

@@ -80,7 +80,7 @@ PyTorch Frame builds directly upon PyTorch, ensuring a smooth transition for exi
   Comes with a collection of readily-usable tabular datasets. Also supports custom datasets to solve your own problem.
   We [benchmark](https://github.com/pyg-team/pytorch-frame/blob/master/benchmark) deep tabular models against GBDTs.
 - **PyTorch integration**:
-  Integrates effortlessly with other PyTorch libraries, facilitating end-to-end training of PyTorch Frame with downstream PyTorch models. For example, by integrating with [PyG](https://pyg.org/), a PyTorch library for GNNs, we can perform deep learning over relational databases. Learn more in [RelBench](https://relbench.stanford.edu/) and [example code (WIP)](https://github.com/snap-stanford/relbench/blob/main/examples/gnn.py).
+  Integrates effortlessly with other PyTorch libraries, facilitating end-to-end training of PyTorch Frame with downstream PyTorch models. For example, by integrating with [PyG](https://pyg.org/), a PyTorch library for GNNs, we can perform deep learning over relational databases. Learn more in [RelBench](https://relbench.stanford.edu/) and [example code](https://github.com/snap-stanford/relbench/blob/main/examples/).
 
 ## Architecture Overview
 

torch_frame/data/dataset.py

@@ -593,7 +593,7 @@ def materialize(
             self._is_materialized = True
             return self
 
-        # 1. Fill column statistics: 
+        # 1. Fill column statistics:
         if col_stats is None:
             # calculate from data if col_stats is not provided
             for col, stype in self.col_to_stype.items():
@@ -616,7 +616,6 @@ def materialize(
         else:
             self._col_stats = col_stats
 
-
         # 2. Create the `TensorFrame`:
         self._to_tensor_frame_converter = self._get_tensorframe_converter()
         self._tensor_frame = self._to_tensor_frame_converter(self.df, device)