code: def _get_iris_as_tensor_dataset(self):
      iris = load_iris()
      df = iris.data
      self.columns = iris.feature_names
      target = iris["target"]
      data = torch.Tensor(df).float()
      labels = torch.Tensor(target).long()
      data_set = TensorDataset(data, labels)
      return data_set
  
file_path: mlflow/examples/pytorch/AxHyperOptimizationPTL/iris_data_module.py
imports: import pytorch_lightning
  import torch
  from sklearn.datasets import load_iris
  from torch.utils.data import DataLoader, TensorDataset, random_split
label_desc: Use image node_id 2 for calling the IrisDataModule obj's underlying member method code with example usage: obj._get_iris_as_tensor_dataset() and returns: data_set