class_docstr: Integration tests for the classical fisher information matrix CFIM
class_docstr_tok: ["Integration","tests","for","the","classical","fisher","information","matrix","CFIM"]
docstr: Testing multiple args to be differentiated using tf
docstr_tok: ["Testing","multiple","args","to","be","differentiated","using","tf"]
code: def test_cfim_multiple_args_tf(self):
      """Testing multiple args to be differentiated using tf"""
      import tensorflow as tf
  
      n_wires = 3
  
      dev = qml.device("default.qubit", wires=n_wires)
  
      @qml.qnode(dev)
      def circ(x, y, z):
          for xi in tf.unstack(x):
              qml.RX(xi, wires=0)
              qml.RX(xi, wires=1)
          for yi in tf.unstack(y):
              qml.RY(yi, wires=0)
              qml.RY(yi, wires=1)
          for zi in tf.unstack(z):
              qml.RZ(zi, wires=0)
              qml.RZ(zi, wires=1)
          return qml.probs(wires=range(n_wires))
  
      x = tf.Variable(np.pi / 8 * np.ones(2), trainable=True)
      y = tf.Variable(np.pi / 8 * np.ones(10), trainable=True)
      z = tf.Variable([1.0], trainable=True)
      cfim = qml.qinfo.classical_fisher(circ)(x, y, z)
      assert np.allclose(cfim[0], 2.0 / 3.0 * np.ones((2, 2)))
      assert np.allclose(cfim[1], 2.0 / 3.0 * np.ones((10, 10)))
      assert np.allclose(cfim[2], np.zeros((1, 1)))
  
imports: import pytest
  import numpy
  import pennylane
  import pennylane.numpy
  from pennylane.qinfo import classical_fisher, quantum_fisher
  from pennylane.qinfo.transforms import _make_probs, _compute_cfim
label_desc: Use image node_id 11 for calling the TestInterfacesClassicalFisher obj's underlying member method code with example usage: obj.test_cfim_multiple_args_tf() and returns: qml