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 multiple parameters of a single Hamiltonian in a tape.
docstr_tok: ["Test","the","tape","generation","for","multiple","parameters","of","a","single","Hamiltonian","in","a","tape","."]
code: def test_single_op_multi_term(self, same_terms):
      """Test the tape generation for multiple parameters of a single Hamiltonian in a tape."""
      import jax
      import jax.numpy as jnp
  
      H = qml.pulse.constant * X(0) + jnp.polyval * (
          X(0) if same_terms else Z(1)
      )
      params = [jnp.array(0.4), jnp.array([0.3, 0.2, 0.1])]
      tape = self.make_tape([H], [params])
      cache = {"total_num_tapes": 10}
      # The Hamiltonian above has two parametrized terms, generated by PauliX(0) and
      # by PauliX(0) or PauliZ(1), depending on whether same_terms is True.
      # For same_terms=True, we expect 2 tapes both with an inserted PauliRot about PauliX(0),
      # but with differing angles. For same_terms=False, there are two more tapes, with
      # inserted rotations about PauliZ(1). This will yield one (two) partial derivative(s)
      # with respect to PauliX(0) (and PauliZ(1)). According to the parameter shapes
      # () and (3,) of H and the one-entry (two-entry) derivative, we expect coefficients
      # with shapes () and (3,) (with shapes (2,) and (2, 3)).
      # First trainable parameter - everything is computed
      (
          grad_tapes,
          (start, end, coeffs),
          cache,
      ) = _generate_tapes_and_coeffs(tape, 0, self.atol, cache)
  
      num_tapes = 2 if same_terms else 4
      words = ["X"] if same_terms else ["IZ", "XI"]
      wires = 0 if same_terms else [0, 1]
      if same_terms:
          exp_coeffs = [
              [jnp.array(2 * self.T)],
              [2 * jax.grad(integral_of_polyval)(params[1], self.T)],
          ]
      else:
          exp_coeffs = [
              [jnp.array(0), jnp.array(2 * self.T)],
              [
                  2 * jax.grad(integral_of_polyval)(params[1], self.T),
                  jnp.zeros(3),
              ],
          ]
      expected = (
          10,
          10 + num_tapes,
          num_tapes,
          words,
          wires,
          tape,
          0,
          exp_coeffs[0],
      )
      exp_cache = {
          "total_num_tapes": 10 + num_tapes,
          0: (10, 10 + num_tapes, exp_coeffs),
      }
  
      self.check_tapes_and_coeffs_equality(
          grad_tapes, (start, end, coeffs), expected
      )
      self.check_cache_equality(cache, exp_cache)
  
      # Second trainable parameter - everything is cached
      (
          grad_tapes,
          (start, end, coeffs),
          cache,
      ) = _generate_tapes_and_coeffs(tape, 1, self.atol, cache)
      expected = (
          10,
          10 + num_tapes,
          0,
          [],
          None,
          tape,
          None,
          exp_coeffs[1],
      )
  
      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 6 for calling the TestGenerateTapesAndCoeffs obj's underlying member method code with example usage: obj.test_single_op_multi_term(same_terms) without return types