code: def input_data_targettest(request):
      if request.param == "series_binary":
          return pd.Series([1, -1, -1, 1])
      elif request.param == "series_multiclass":
          return pd.Series([1, 0, 2])
      elif request.param == "series_multilabel":
          return pd.Series([[1, 0], [0, 1]])
      elif request.param == "series_continuous":
          return pd.Series([0.1, 0.6, 0.7])
      elif request.param == "series_continuous-multioutput":
          return pd.Series([[1.5, 2.0], [3.0, 1.6]])
      elif request.param == "pandas_binary":
          return pd.DataFrame([1, -1, -1, 1])
      elif request.param == "pandas_multiclass":
          return pd.DataFrame([1, 0, 2])
      elif request.param == "pandas_multilabel":
          return pd.DataFrame([[1, 0], [0, 1]])
      elif request.param == "pandas_continuous":
          return pd.DataFrame([0.1, 0.6, 0.7])
      elif request.param == "pandas_continuous-multioutput":
          return pd.DataFrame([[1.5, 2.0], [3.0, 1.6]])
      elif request.param == "numpy_binary":
          return np.array([1, -1, -1, 1])
      elif request.param == "numpy_multiclass":
          return np.array([1, 0, 2])
      elif request.param == "numpy_multilabel":
          return np.array([[1, 0], [0, 1]])
      elif request.param == "numpy_continuous":
          return np.array([0.1, 0.6, 0.7])
      elif request.param == "numpy_continuous-multioutput":
          return np.array([[1.5, 2.0], [3.0, 1.6]])
      elif request.param == "list_binary":
          return [1, -1, -1, 1]
      elif request.param == "list_multiclass":
          return [1, 0, 2]
      elif request.param == "list_multilabel":
          return [[0, 1], [1, 0]]
      elif request.param == "list_continuous":
          return [0.1, 0.6, 0.7]
      elif request.param == "list_continuous-multioutput":
          return [[1.5, 2.0], [3.0, 1.6]]
      elif "openml" in request.param:
          _, openml_id = request.param.split("_")
          X, y = sklearn.datasets.fetch_openml(
              data_id=int(openml_id), return_X_y=True, as_frame=True
          )
          if (
              len(y.shape) > 1
              and y.shape[1] > 1
              and np.any(y.eq("TRUE").any(1).to_numpy())
          ):
              # This 'if' is only asserted for multi-label data
              # Force the downloaded data to be interpreted as multilabel
              y = y.dropna()
              y.replace("FALSE", 0, inplace=True)
              y.replace("TRUE", 1, inplace=True)
              y = y.astype(int)
          return y
      elif "sparse" in request.param:
          # We expect the names to be of the type sparse_csc_nonan
          sparse_, type_, nan_ = request.param.split("_")
          if "nonan" in nan_:
              data = np.ones(3)
          else:
              data = np.array([1, 2, np.nan])
  
          # Then the type of sparse
          if "csc" in type_:
              return sparse.csc_matrix(data)
          elif "csr" in type_:
              return sparse.csr_matrix(data)
          elif "coo" in type_:
              return sparse.coo_matrix(data)
          elif "bsr" in type_:
              return sparse.bsr_matrix(data)
          elif "lil" in type_:
              return sparse.lil_matrix(data)
          elif "dok" in type_:
              return sparse.dok_matrix(np.vstack((data, data, data)))
          elif "dia" in type_:
              return sparse.dia_matrix(np.vstack((data, data, data)))
          else:
              ValueError(
                  "Unsupported indirect fixture {}".format(
                      request.param
                  )
              )
      else:
          ValueError(
              "Unsupported indirect fixture {}".format(request.param)
          )
  
imports: import numpy
  import pandas
  import sklearn.datasets
  import sklearn.model_selection
  from pandas.api.types import is_bool_dtype, is_numeric_dtype
  from scipy import sparse
  from sklearn.utils.multiclass import type_of_target
  from autosklearn.data.target_validator import TargetValidator
  import pytest
label_desc: Use image node_id 1 for calling a global function with example usage: input_data_targettest(request) and returns: pd, pd, pd, pd, pd, pd, pd, pd, pd, pd, np, np, np, np, np, list, list, list, list, list, y, sparse, sparse, sparse, sparse, sparse, sparse, sparse