code: def test_distributions(storage_mode: str) -> None:
      with StorageSupplier(storage_mode) as storage:
          if dist.get_rank() == 0:
              study = optuna.create_study(storage=storage)
              trial = TorchDistributedTrial(study.ask())
          else:
              trial = TorchDistributedTrial(None)
  
          trial.suggest_float("u", 0, 1)
          trial.suggest_float("lu", 1e-7, 1, log=True)
          trial.suggest_float("du", 0, 1, step=0.5)
          trial.suggest_int("i", 0, 1)
          trial.suggest_int("il", 1, 128, log=True)
          trial.suggest_categorical("c", ("a", "b", "c"))
  
          distributions = trial.distributions
          assert distributions[
              "u"
          ] == optuna.distributions.FloatDistribution(0, 1)
          assert distributions[
              "lu"
          ] == optuna.distributions.FloatDistribution(1e-7, 1, log=True)
          assert distributions[
              "du"
          ] == optuna.distributions.FloatDistribution(0, 1, step=0.5)
          assert distributions[
              "i"
          ] == optuna.distributions.IntDistribution(0, 1)
          assert distributions[
              "il"
          ] == optuna.distributions.IntDistribution(1, 128, log=True)
          assert distributions[
              "c"
          ] == optuna.distributions.CategoricalDistribution(
              ("a", "b", "c")
          )
  
imports: import datetime
  import os
  from typing import Optional
  import pytest
  import optuna
  from optuna._imports import try_import
  from optuna.integration import TorchDistributedTrial
  from optuna.testing.pruners import DeterministicPruner
  from optuna.testing.storages import StorageSupplier
label_desc: Use image node_id 18 for calling a global function with example usage: test_distributions(storage_mode) without return types