class_docstr: Test working cases of stoch_pulse_grad.
class_docstr_tok: ["Test","working","cases","of","stoch_pulse_grad","."]
docstr: Test that a zero gradient is returned for trainable parameters that are
  identified to have a zero gradient in advance.
docstr_tok: ["Test","that","a","zero","gradient","is","returned","for","trainable","parameters","that","are","identified","to","have","a","zero","gradient","in","advance","."]
code: def test_some_zero_grads(self, dev_name):
      """Test that a zero gradient is returned for trainable parameters that are
      identified to have a zero gradient in advance."""
      import jax
      import jax.numpy as jnp
  
      jax.config.update("jax_enable_x64", True)
      ops = [
          qml.evolve(qml.pulse.pwc(0.1) * qml.PauliX(w))(
              [jnp.linspace(1.0, 2.0, 5)], 0.1
          )
          for w in [1, 0]
      ]
      measurements = [qml.expval(qml.PauliZ(0)), qml.probs(wires=0)]
      tape = qml.tape.QuantumScript(ops, measurements)
      tapes, fn = stoch_pulse_grad(tape, num_split_times=3)
      assert len(tapes) == 2 * 3
  
      dev = qml.device(dev_name, wires=2)
      res = fn(qml.execute(tapes, dev, None))
      assert isinstance(res, tuple) and len(res) == 2
      assert qml.math.allclose(res[0][0], np.zeros(5))
      assert qml.math.allclose(res[1][0], np.zeros((2, 5)))
      jax.clear_caches()
  
imports: import warnings
  import copy
  import pytest
  import numpy
  import pennylane
  from pennylane.gradients.general_shift_rules import eigvals_to_frequencies, generate_shift_rule
  from pennylane.gradients.pulse_gradient import _parshift_and_integrate, _split_evol_ops, _split_evol_tape, stoch_pulse_grad
label_desc: Use image node_id 3 for calling the TestStochPulseGrad obj's underlying member method code with example usage: obj.test_some_zero_grads(dev_name) without return types