[Misc] Reorder the variables in minibatch.py. (#6275)

Co-authored-by: Ubuntu <ubuntu@ip-172-31-28-63.ap-northeast-1.compute.internal>

python/dgl/graphbolt/minibatch.py

@@ -21,40 +21,6 @@ class MiniBatch:
     representation of input and output data across different stages, ensuring
     consistency and ease of use throughout the loading process."""
 
-    sampled_subgraphs: List[SampledSubgraph] = None
-    """A list of 'SampledSubgraph's, each one corresponding to one layer,
-    representing a subset of a larger graph structure.
-    """
-
-    node_features: Union[
-        Dict[str, torch.Tensor], Dict[Tuple[str, str], torch.Tensor]
-    ] = None
-    """A representation of node features.
-      - If keys are single strings: It means the graph is homogeneous, and the
-      keys are feature names.
-      - If keys are tuples: It means the graph is heterogeneous, and the keys
-      are tuples of '(node_type, feature_name)'.
-    """
-
-    edge_features: List[
-        Union[Dict[str, torch.Tensor], Dict[Tuple[str, str], torch.Tensor]]
-    ] = None
-    """Edge features associated with the 'sampled_subgraphs'.
-      - If keys are single strings: It means the graph is homogeneous, and the
-      keys are feature names.
-      - If keys are tuples: It means the graph is heterogeneous, and the keys
-      are tuples of '(edge_type, feature_name)'. Note, edge type is single
-      string of format 'str:str:str'.
-    """
-
-    input_nodes: Union[torch.Tensor, Dict[str, torch.Tensor]] = None
-    """A representation of input nodes in the outermost layer. Conatins all nodes
-       in the 'sampled_subgraphs'.
-    - If `input_nodes` is a tensor: It indicates the graph is homogeneous.
-    - If `input_nodes` is a dictionary: The keys should be node type and the
-      value should be corresponding heterogeneous node id.
-    """
-
     seed_nodes: Union[torch.Tensor, Dict[str, torch.Tensor]] = None
     """
     Representation of seed nodes used for sampling in the graph.
@@ -63,15 +29,6 @@ class MiniBatch:
       value should be corresponding heterogeneous node ids.
     """
 
-    labels: Union[torch.Tensor, Dict[str, torch.Tensor]] = None
-    """
-    labelss associated with seed nodes in the graph.
-    - If `labels` is a tensor: It indicates the graph is homogeneous. The value
-      should be corresponding labelss to given 'seed_nodes' or 'node_pairs'.
-    - If `labels` is a dictionary: The keys should be node or edge type and the
-      value should be corresponding labelss to given 'seed_nodes' or 'node_pairs'.
-    """
-
     node_pairs: Union[
         Tuple[torch.Tensor, torch.Tensor],
         Dict[str, Tuple[torch.Tensor, torch.Tensor]],
@@ -85,6 +42,15 @@ class MiniBatch:
       type.
     """
 
+    labels: Union[torch.Tensor, Dict[str, torch.Tensor]] = None
+    """
+    Labels associated with seed nodes / node pairs in the graph.
+    - If `labels` is a tensor: It indicates the graph is homogeneous. The value
+      should be corresponding labels to given 'seed_nodes' or 'node_pairs'.
+    - If `labels` is a dictionary: The keys should be node or edge type and the
+      value should be corresponding labels to given 'seed_nodes' or 'node_pairs'.
+    """
+
     negative_srcs: Union[torch.Tensor, Dict[str, torch.Tensor]] = None
     """
     Representation of negative samples for the head nodes in the link
@@ -105,6 +71,40 @@ class MiniBatch:
       given type.
     """
 
+    sampled_subgraphs: List[SampledSubgraph] = None
+    """A list of 'SampledSubgraph's, each one corresponding to one layer,
+    representing a subset of a larger graph structure.
+    """
+
+    input_nodes: Union[torch.Tensor, Dict[str, torch.Tensor]] = None
+    """A representation of input nodes in the outermost layer. Conatins all nodes
+       in the 'sampled_subgraphs'.
+    - If `input_nodes` is a tensor: It indicates the graph is homogeneous.
+    - If `input_nodes` is a dictionary: The keys should be node type and the
+      value should be corresponding heterogeneous node id.
+    """
+
+    node_features: Union[
+        Dict[str, torch.Tensor], Dict[Tuple[str, str], torch.Tensor]
+    ] = None
+    """A representation of node features.
+      - If keys are single strings: It means the graph is homogeneous, and the
+      keys are feature names.
+      - If keys are tuples: It means the graph is heterogeneous, and the keys
+      are tuples of '(node_type, feature_name)'.
+    """
+
+    edge_features: List[
+        Union[Dict[str, torch.Tensor], Dict[Tuple[str, str], torch.Tensor]]
+    ] = None
+    """Edge features associated with the 'sampled_subgraphs'.
+      - If keys are single strings: It means the graph is homogeneous, and the
+      keys are feature names.
+      - If keys are tuples: It means the graph is heterogeneous, and the keys
+      are tuples of '(edge_type, feature_name)'. Note, edge type is single
+      string of format 'str:str:str'.
+    """
+
     compacted_node_pairs: Union[
         Tuple[torch.Tensor, torch.Tensor],
         Dict[str, Tuple[torch.Tensor, torch.Tensor]],