code: def test_onehot_category_specific_cases():
      # See this for reasoning: https://github.com/rapidsai/cuml/issues/2690
  
      # All of these cases use sparse=False, where
      # test_onehot_category_class_count uses sparse=True
  
      # ==== 2 Rows (Low before High) ====
      example_df = DataFrame()
      example_df["low_cardinality_column"] = ["A"] * 200 + ["B"] * 56
      example_df["high_cardinality_column"] = cp.linspace(0, 255, 256)
  
      encoder = OneHotEncoder(handle_unknown="ignore", sparse=False)
      encoder.fit_transform(example_df)
  
      # ==== 2 Rows (High before Low, used to fail) ====
      example_df = DataFrame()
      example_df["high_cardinality_column"] = cp.linspace(0, 255, 256)
      example_df["low_cardinality_column"] = ["A"] * 200 + ["B"] * 56
  
      encoder = OneHotEncoder(handle_unknown="ignore", sparse=False)
      encoder.fit_transform(example_df)
  
imports: from sklearn.preprocessing import OneHotEncoder
  from cuml.testing.utils import stress_param, from_df_to_numpy, assert_inverse_equal, generate_inputs_from_categories
  from cuml.preprocessing import OneHotEncoder
  from cuml.internals.safe_imports import gpu_only_import_from
  import pytest
  from cuml.internals.safe_imports import cpu_only_import
  import math
  from cuml.internals.safe_imports import gpu_only_import
label_desc: Use image node_id 16 for calling a global function with example usage: test_onehot_category_specific_cases() without return types