code: def test_svm_predict_convert_dtype(
      train_dtype, test_dtype, classifier
  ):
      X, y = make_classification(n_samples=50, random_state=0)
  
      X = X.astype(train_dtype)
      y = y.astype(train_dtype)
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, train_size=0.8, random_state=0
      )
  
      if classifier:
          clf = cu_svm.SVC()
      else:
          clf = cu_svm.SVR()
      clf.fit(X_train, y_train)
      clf.predict(X_test.astype(test_dtype))
  
imports: import platform
  from sklearn.preprocessing import StandardScaler
  from sklearn.model_selection import train_test_split
  from sklearn.metrics import mean_squared_error
  from sklearn.datasets import make_classification, make_gaussian_quantiles
  from sklearn.datasets import make_regression, make_friedman1
  from sklearn.datasets import load_iris, make_blobs
  from sklearn import svm
  from cuml.testing.utils import unit_param, quality_param, stress_param, compare_svm, compare_probabilistic_svm, svm_array_equal
  from cuml.common import input_to_cuml_array
  import cuml.svm
  import cuml
  from cuml.internals.safe_imports import gpu_only_import_from
  from cuml.internals.safe_imports import cpu_only_import
  import pytest
  from cuml.internals.safe_imports import gpu_only_import
label_desc: Use image node_id 19 for calling a global function with example usage: test_svm_predict_convert_dtype(train_dtype, test_dtype, classifier) without return types