class_docstr: Test the utility function _generate_tapes_and_coeffs.
class_docstr_tok: ["Test","the","utility","function","_generate_tapes_and_coeffs","."]
docstr: Test the tape generation for a single parameter and Hamiltonian term in a tape.
docstr_tok: ["Test","the","tape","generation","for","a","single","parameter","and","Hamiltonian","term","in","a","tape","."]
code: def test_single_op_single_term(self, add_constant):
      """Test the tape generation for a single parameter and Hamiltonian term in a tape."""
      import jax.numpy as jnp
  
      H = qml.pulse.constant * X(0)
      if add_constant:
          H = 0.4 * Z(1) + H
      params = [jnp.array(0.4)]
      tape = self.make_tape([H], [params])
      cache = {"total_num_tapes": -5}
      (
          grad_tapes,
          (start, end, coeffs),
          cache,
      ) = _generate_tapes_and_coeffs(tape, 0, self.atol, cache)
  
      # The Hamiltonian above has a single parametrized term, generated by PauliX(0).
      # We therefore expect 2 tapes both with an inserted PauliRot about PauliX(0), but
      # with differing angles. This will yield a single partial derivative with respect to
      # PauliX(0). According to the single scalar parameter of H and the single
      # derivative, we expect a single coefficient which is a scalar and has value 2 * T.
      words = ["IX"] if add_constant else ["X"]
      wires = [1, 0] if add_constant else 0
      # There is a factor of 2 for the exp(i*(pauliword)) to PauliRot conversion
      exp_coeffs = [jnp.array(2 * self.T)]
      expected = (-5, -3, 2, words, wires, tape, 0, exp_coeffs)
      exp_cache = {"total_num_tapes": -3, 0: (-5, -3, [coeffs])}
  
      self.check_tapes_and_coeffs_equality(
          grad_tapes, (start, end, coeffs), expected
      )
      self.check_cache_equality(cache, exp_cache)
  
imports: import copy
  import pytest
  import numpy
  import pennylane
  from pennylane import numpy
  from pennylane.ops.qubit.special_unitary import pauli_basis_matrices, pauli_basis_strings
  from pennylane.math import expand_matrix
  from pennylane.gradients.pulse_gradient_odegen import _generate_tapes_and_coeffs, pulse_odegen, _insert_op, _nonzero_coeffs_and_words, _one_parameter_generators, _one_parameter_paulirot_coeffs, _parshift_and_contract
label_desc: Use image node_id 5 for calling the TestGenerateTapesAndCoeffs obj's underlying member method code with example usage: obj.test_single_op_single_term(add_constant) without return types