code: def _transform(
      self,
      data: _typing.Union[
          autogl.data.graph.GeneralStaticGraph, _typing.Any
      ],
  ) -> _typing.Union[autogl.data.graph.GeneralStaticGraph, _typing.Any]:
      if isinstance(data, autogl.data.graph.GeneralStaticGraph):
          for node_t in data.nodes:
              for candidate_feature_key in ("feat", "x"):
                  if candidate_feature_key in data.nodes[node_t].data:
                      data.nodes[node_t].data[
                          candidate_feature_key
                      ] = torch.ones(
                          (
                              data.nodes[node_t]
                              .data[candidate_feature_key]
                              .size(0),
                              1,
                          )
                      ).to(
                          data.nodes[node_t].data[candidate_feature_key]
                      )
                  elif len(data.nodes[node_t].data) > 0:
                      _ref = data.nodes[node_t].data[
                          list(data.nodes[node_t].data)[0]
                      ]
                      data.nodes[node_t].data[
                          candidate_feature_key
                      ] = torch.ones((_ref.size(0), 1)).to(_ref)
                  else:
                      data.nodes[node_t].data[
                          candidate_feature_key
                      ] = torch.ones(
                          (
                              torch.unique(data.edges.connections).size(
                                  0
                              ),
                              1,
                          )
                      )
      elif hasattr(data, "x") and isinstance(data.x, torch.Tensor):
          data.x = torch.ones((data.x.shape[0], 1)).to(data.x)
      elif hasattr(data, "edge_index") and isinstance(
          data.edge_index, torch.Tensor
      ):
          data.x = torch.ones(
              (torch.unique(data.edge_index).size(0), 1)
          ).to(data.edge_index)
      else:
          raise ValueError("Unsupported provided data")
      return data
  
imports: import time
  import numpy
  import torch
  import typing
  from sklearn import preprocessing
  from sklearn.metrics.pairwise import cosine_similarity
  import tqdm
  import tabulate
  import autogl.data.graph
  from ._base_feature_engineer import BaseFeatureEngineer
  from ._feature_engineer_registry import FeatureEngineerUniversalRegistry
  from ._selectors import GBDTFeatureSelector
  from ...utils import get_logger
label_desc: Use image node_id 1 for calling the OnlyConstFeature obj's underlying member method code with example usage: obj._transform(data) and returns: data