class_docstr: The dataset consists of 11 diverse multilingual legal NLU tasks.
  6 tasks have one single configuration and 5 tasks have two or three configurations.
  This leads to a total of 18 tasks (8 single-label text classification tasks,
  5 multi-label text classification tasks and 5 token-classification tasks).
  Find more information on the dataset here: https://huggingface.co/datasets/joelito/lextreme
  
  We prompt models using the following format (example for german_argument_mining)
  
      <sentence>
      Urteilsstil:
  
      Target completion:
          <sentence> (<sentence>:conclusion, subsumption, definition or other)
  
  Using an example from the training dataset, we have
  
  ```
  Die Klage ist hinsichtlich der begehrten „Umzugkosten“ und hinsichtlich der begehrten
  „Übernahme der durch den Rechtsstreit gegen das Jobcenter verursachten tatsächlichen Kosten“ insgesamt unzulässig.
  
  Urteilsstil:
  Target completion:
      conclusion
  ```
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code: def get_instances(self) -> List[Instance]:
      instances = []
      for subset in self.subsets:
          instances.extend(self.get_instances_for_subset(subset))
      return instances
  
file_path: H2O/h2o_hf/helm/src/helm/benchmark/scenarios/lextreme_scenario.py
imports: import ast
  import random
  from pathlib import Path
  from typing import List, Any
  import datasets
  from datasets import load_dataset
  from .scenario import Scenario, Instance, Reference, CORRECT_TAG, TRAIN_SPLIT, VALID_SPLIT, TEST_SPLIT, Output, Input
label_desc: Use image node_id 3 for calling the LEXTREMEScenario obj's underlying member method code with example usage: obj.get_instances() and returns: instances