code: def example_step() -> None:
      """A very minimalistic pipeline step.
      Loads a sample dataset, trains a simple classifier and logs
      a couple of metrics.
      """
      neptune_run = (
          zenml.integrations.neptune.experiment_trackers.run_state.get_neptune_run()
      )
      digits = load_digits()
      data = digits.images.reshape((len(digits.images), -1))
  
      x_train, x_test, y_train, y_test = train_test_split(
          data, digits.target, test_size=0.3
      )
      gamma = 0.001
      neptune_run["params/gamma"] = gamma
      model = SVC(gamma=gamma)
      model.fit(x_train, y_train)
      test_acc = model.score(x_test, y_test)
      neptune_run["metrics/val_accuracy"] = test_acc
  
file_path: neptune-client/tests/e2e/integrations/test_zenml.py
imports: import hashlib
  import pytest
  from sklearn.datasets import load_digits
  from sklearn.model_selection import train_test_split
  from sklearn.svm import SVC
  import neptune
  from tests.e2e.base import BaseE2ETest
label_desc: Use image node_id 5 for calling a global function with example usage: example_step() without return types