class_docstr: Test the helper routine ``_parshift_and_integrate``. Most tests use uniform
  return types and parameters, so that we can test against simple tensor contractions.
class_docstr_tok: ["Test","the","helper","routine","``","_parshift_and_integrate","``",".","Most","tests","use","uniform","return","types","and","parameters",",","so","that","we","can","test","against","simple","tensor","contractions","."]
docstr: Test that an error is raised if multiple measurements, a shot vector and broadcasting
  all are used simultaneously.
docstr_tok: ["Test","that","an","error","is","raised","if","multiple","measurements",",","a","shot","vector","and","broadcasting","all","are","used","simultaneously","."]
code: def test_raises_multi_measure_multi_shots_broadcasting(self):
      """Test that an error is raised if multiple measurements, a shot vector and broadcasting
      all are used simultaneously."""
  
      _match = "Broadcasting, multiple measurements and shot vectors are currently"
      with pytest.raises(NotImplementedError, match=_match):
          # Dummy input values that are barely used before raising the error.
          _parshift_and_integrate(
              [],
              [],
              [],
              [],
              single_measure=False,
              has_partitioned_shots=True,
              use_broadcasting=True,
          )
  
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 7 for calling the TestParshiftAndIntegrate obj's underlying member method code with example usage: obj.test_raises_multi_measure_multi_shots_broadcasting() without return types