I’ve tried using that parameter:
x1 = T.matrix('X')
n1 = T.iscalar('n')
x1.tag.test_value = np.empty_like(X_test1[:100])
n1.tag.test_value = 100
_sample_proba_nn1 = approx.sample_node(bsnn.out1.distribution.p,
size = n1,
more_replacements = {bsnn['bnn_input1']: x1)
But more_replacements is not accepting a dictionary with more than 1 element.