Fix and add missing syntax highlighting for code blocks in documentation

docs/source/tutorial/distributed_pyg.rst

@@ -79,7 +79,7 @@ Later on, each node in the cluster then owns a single partition of this graph.
 
 The resulting structure of partitioning for a two-part split on the homogeneous :obj:`ogbn-products` is shown below:
 
-.. code-block::
+.. code-block:: none
 
     partitions
     └─ obgn-products

torch_geometric/data/lightning/datamodule.py

@@ -221,7 +221,7 @@ class LightningDataset(LightningDataModule):
         speed.html>`__ are supported in order to correctly share data across
         all devices/processes:
 
-        .. code-block::
+        .. code-block:: python
 
             import pytorch_lightning as pl
             trainer = pl.Trainer(strategy="ddp_spawn", accelerator="gpu",
@@ -332,7 +332,7 @@ class LightningNodeData(LightningData):
         speed.html>`__ are supported in order to correctly share data across
         all devices/processes:
 
-        .. code-block::
+        .. code-block:: python
 
             import pytorch_lightning as pl
             trainer = pl.Trainer(strategy="ddp_spawn", accelerator="gpu",
@@ -525,7 +525,7 @@ class LightningLinkData(LightningData):
         speed.html>`__ are supported in order to correctly share data across
         all devices/processes:
 
-        .. code-block::
+        .. code-block:: python
 
             import pytorch_lightning as pl
             trainer = pl.Trainer(strategy="ddp_spawn", accelerator="gpu",

torch_geometric/distributed/partition.py

@@ -23,7 +23,7 @@ class Partitioner:
 
     **Homogeneous graphs:**
 
-    .. code-block::
+    .. code-block:: none
 
         root/
         |-- META.json
@@ -40,7 +40,7 @@ class Partitioner:
 
     **Heterogeneous graphs:**
 
-    .. code-block::
+    .. code-block:: none
 
         root/
         |-- META.json

torch_geometric/nn/aggr/base.py

@@ -25,7 +25,7 @@ class Aggregation(torch.nn.Module):
     Notably, :obj:`index` does not have to be sorted (for most aggregation
     operators):
 
-    .. code-block::
+    .. code-block:: python
 
        # Feature matrix holding 10 elements with 64 features each:
        x = torch.randn(10, 64)
@@ -39,7 +39,7 @@ class Aggregation(torch.nn.Module):
     called :obj:`ptr`. Here, elements within the same set need to be grouped
     together in the input, and :obj:`ptr` defines their boundaries:
 
-    .. code-block::
+    .. code-block:: python
 
        # Feature matrix holding 10 elements with 64 features each:
        x = torch.randn(10, 64)

torch_geometric/transforms/node_property_split.py

@@ -44,7 +44,6 @@ class NodePropertySplit(BaseTransform):
             of the node property, so that nodes with greater values of the
             property are considered to be OOD (default: :obj:`True`)
 
-    Example:
     .. code-block:: python
 
         from torch_geometric.transforms import NodePropertySplit

torch_geometric/transforms/pad.py

@@ -269,8 +269,7 @@ class Pad(BaseTransform):
     All the attributes of edge types other than these two are padded using a
     value of :obj:`1.5`.
 
-    Example:
-    .. code-block::
+    .. code-block:: python
 
         num_nodes = {'v0': 10, 'v1': 20, 'v2':30}
         num_edges = {('v0', 'e0', 'v1'): 80}

torch_geometric/transforms/random_link_split.py

@@ -23,7 +23,7 @@ class RandomLinkSplit(BaseTransform):
     in validation and test splits; and the validation split does not include
     edges in the test split.
 
-    .. code-block::
+    .. code-block:: python
 
         from torch_geometric.transforms import RandomLinkSplit