code: def ogbn_data_to_general_static_graph(
      cls,
      ogbn_data: _typing.Mapping[str, _typing.Union[np.ndarray, int]],
      nodes_label: np.ndarray = ...,
      nodes_label_key: str = ...,
      train_index: _typing.Optional[np.ndarray] = ...,
      val_index: _typing.Optional[np.ndarray] = ...,
      test_index: _typing.Optional[np.ndarray] = ...,
      nodes_data_key_mapping: _typing.Optional[
          _typing.Mapping[str, str]
      ] = ...,
      edges_data_key_mapping: _typing.Optional[
          _typing.Mapping[str, str]
      ] = ...,
      graph_data_key_mapping: _typing.Optional[
          _typing.Mapping[str, str]
      ] = ...,
  ) -> GeneralStaticGraph:
      # TODO
      edge_index = ogbn_data["edge_index"]
      num_nodes = ogbn_data["num_nodes"]
      edge_feat = ogbn_data["edge_feat"]
      if edge_feat is not None:
          edge_feat = torch.tensor(edge_feat)
      edge_index = SparseTensor(
          row=torch.tensor(edge_index[0]),
          col=torch.tensor(edge_index[1]),
          value=edge_feat,
          sparse_sizes=(num_nodes, num_nodes),
      )
      _, _, value = edge_index.coo()
      if value is not None:
          ogbn_data["edge_feat"] = value.cpu().detach().numpy()
      else:
          ogbn_data["edge_feat"] = edge_feat
      edge_index = edge_index.to_symmetric()
      row, col, _ = edge_index.coo()
      edge_index = np.array(
          [row.cpu().detach().numpy(), col.cpu().detach().numpy()]
      )
      homogeneous_static_graph: GeneralStaticGraph = GeneralStaticGraphGenerator.create_homogeneous_static_graph(
          dict(
              [
                  (target_key, torch.from_numpy(ogbn_data[source_key]))
                  for source_key, target_key in nodes_data_key_mapping.items()
              ]
          ),
          torch.tensor(edge_index),
          dict(
              [
                  (target_key, torch.from_numpy(ogbn_data[source_key]))
                  for source_key, target_key in edges_data_key_mapping.items()
              ]
          )
          if isinstance(edges_data_key_mapping, _typing.Mapping)
          else ...,
          dict(
              [
                  (target_key, torch.from_numpy(ogbn_data[source_key]))
                  for source_key, target_key in graph_data_key_mapping.items()
              ]
          )
          if isinstance(graph_data_key_mapping, _typing.Mapping)
          else ...,
      )
      if isinstance(nodes_label, np.ndarray) and isinstance(
          nodes_label_key, str
      ):
          if " " in nodes_label_key:
              raise ValueError("Illegal nodes label key")
          homogeneous_static_graph.nodes.data[
              nodes_label_key
          ] = torch.from_numpy(nodes_label.squeeze()).squeeze()
      if isinstance(train_index, np.ndarray):
          homogeneous_static_graph.nodes.data[
              "train_mask"
          ] = index_to_mask(
              torch.from_numpy(train_index), ogbn_data["num_nodes"]
          )
      if isinstance(val_index, np.ndarray):
          homogeneous_static_graph.nodes.data[
              "val_mask"
          ] = index_to_mask(
              torch.from_numpy(val_index), ogbn_data["num_nodes"]
          )
      if isinstance(test_index, np.ndarray):
          homogeneous_static_graph.nodes.data[
              "test_mask"
          ] = index_to_mask(
              torch.from_numpy(test_index), ogbn_data["num_nodes"]
          )
      return homogeneous_static_graph
  
imports: import numpy
  import torch
  import typing
  from autogl import backend
  from torch_sparse import SparseTensor
  from autogl import backend
  from autogl.data import InMemoryStaticGraphSet
  from autogl.data.graph import GeneralStaticGraph, GeneralStaticGraphGenerator
  from ._dataset_registry import DatasetUniversalRegistry
  from .utils import index_to_mask
label_desc: Use image node_id 1 for calling the _OGBNDatasetUtil obj's underlying member method code with example usage: obj.ogbn_data_to_general_static_graph(cls, ogbn_data, nodes_label, nodes_label_key, train_index, val_index, test_index, nodes_data_key_mapping, edges_data_key_mapping, graph_data_key_mapping) and returns: homogeneous_static_graph