def make_classifiers():
names = ["<STR_LIT>", "<STR_LIT>", "<STR_LIT>", "<STR_LIT>", "<STR_LIT>", "<STR_LIT>"]<EOL>nh = <NUM_LIT:10><EOL>sinsq = (lambda x: np.power(np.sin(x), <NUM_LIT>))<EOL>srhl_sinsq = MLPRandomLayer(n_hidden=nh, activation_func=sinsq)<EOL>srhl_tanh = MLPRandomLayer(n_hidden=nh, activation_func='<STR_LIT>')<EOL>srhl_tribas = MLPRandomLayer(n_hidden=nh, activation_func='<STR_LIT>')<EOL>srhl_hardlim = MLPRandomLayer(n_hidden=nh, activation_func='<STR_LIT>')<EOL>srhl_rbf = RBFRandomLayer(n_hidden=nh*<NUM_LIT:2>, rbf_width=<NUM_LIT:0.1>, random_state=<NUM_LIT:0>)<EOL>log_reg = LogisticRegression()<EOL>classifiers = [GenELMClassifier(hidden_layer=srhl_tanh),<EOL>GenELMClassifier(hidden_layer=srhl_tanh, regressor=log_reg),<EOL>GenELMClassifier(hidden_layer=srhl_sinsq),<EOL>GenELMClassifier(hidden_layer=srhl_tribas),<EOL>GenELMClassifier(hidden_layer=srhl_hardlim),<EOL>GenELMClassifier(hidden_layer=srhl_rbf)]<EOL>return names, classifiers<EOL>
:return: