code: def test_custom_pipeline_positions(pos):
      """Assert that a custom pipeline can be provided at a specific position."""
      data = pycaret.datasets.get_data("cancer")
      pc = pycaret.classification.setup(
          data=data,
          remove_outliers=True,
          remove_multicollinearity=True,
          custom_pipeline=[("scaler", StandardScaler())],
          custom_pipeline_position=pos,
      )
      # The last element is always CleanColumnNames.
      if pos < 0:
          pos -= 1
      assert pc.pipeline.steps[pos][0] == "scaler"
  
imports: import io
  import numpy
  import pandas
  import pytest
  from imblearn.over_sampling import ADASYN
  from scipy.sparse import csr_matrix
  from sklearn.decomposition import PCA
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import StandardScaler
  import pycaret.classification
  import pycaret.datasets
  import pycaret.regression
label_desc: Use image node_id 43 for calling a global function with example usage: test_custom_pipeline_positions(pos) without return types