code: def load_data(
      self,
      dataset: Dataset,
      target_formatter: Optional[TargetFormatter] = None,
  ) -> Dataset:
      if not self.predicting:
          self.num_features = _get_num_features(
              self.load_sample(dataset[0])
          )
  
          if isinstance(dataset, InMemoryDataset):
              self.load_target_metadata(
                  [sample.y for sample in dataset], target_formatter
              )
          else:
              self.load_target_metadata(None, target_formatter)
  
          if hasattr(dataset, "num_classes"):
              self.num_classes = dataset.num_classes
      return dataset
  
file_path: lightning-flash/src/flash/graph/classification/input.py
imports: from typing import Any, Dict, Mapping, Optional
  from torch.utils.data import Dataset
  from flash.core.data.io.classification_input import ClassificationInputMixin
  from flash.core.data.io.input import DataKeys, Input
  from flash.core.data.utilities.classification import TargetFormatter
  from flash.core.data.utilities.samples import to_sample
  from flash.core.utilities.imports import _TOPIC_GRAPH_AVAILABLE, requires
label_desc: Use image node_id 1 for calling the GraphClassificationDatasetInput obj's underlying member method code with example usage: obj.load_data(dataset, target_formatter) and returns: dataset