code: def create_X(instances=1000, n_feats=10, categs_per_feat=5, seed=0):
      rs = np.random.RandomState(seed)
      size = (instances, n_feats)
      X = rs.randint(0, categs_per_feat, size=size)
      return X
  
file_path: auto-sklearn/test/test_pipeline/components/data_preprocessing/test_one_hot_encoding.py
imports: import numpy
  from scipy import sparse
  from autosklearn.pipeline.components.data_preprocessing.categorical_encoding.no_encoding import NoEncoding
  from autosklearn.pipeline.components.data_preprocessing.categorical_encoding.one_hot_encoding import OneHotEncoder
  from autosklearn.pipeline.util import _test_preprocessing
  import unittest
label_desc: Use image node_id 1 for calling a global function with example usage: create_X(instances, n_feats, categs_per_feat, seed) and returns: X