code: def _one_parameter_paulirot_coeffs(generators, num_wires):
      r"""Compute the Pauli coefficients of effective generators. The coefficients correspond
      to the decomposition into (rescaled) Pauli word generators as used by ``PauliRot``
      gates.
  
      Args:
          generators (tuple[tensor_like]): Generators of the one-parameter groups that
              reproduce the partial derivatives of the parametrized evolution of interest.
          num_wires (int): Number of wires the parametrized evolution acts on.
  
      Returns:
          tuple[tensor_like]: Coefficients of the provided generators in the Pauli basis,
          modified by a factor of ``2j`` (see warning below).
  
      .. warning::
  
          This function includes a prefactor ``2j`` in its output compared to the "standard" Pauli
          basis coefficients. That is, for the effective generator :math:`\frac{1}{3}iX`, which has
          the Pauli basis coefficient :math:`\frac{1}{3}i`, this function will compute the
          number :math:`-\frac{2}{3}`. This is in order to accomodate the use of ``qml.PauliRot``
          in the pulse generator differentiation pipeline.
  
      """
      # The generators are skew-Hermitian. Therefore _pauli_decompose will return a purely
      # imaginary tensor. Multiplying with 2i results in a real-valued tensor, which
      # we prefer over a complex-valued tensor with vanishing imaginary part
      return tuple(
          qml.math.real(2j * _pauli_decompose(g, num_wires))
          for g in generators
      )
  
file_path: pennylane/pennylane/gradients/pulse_gradient_odegen.py
imports: from typing import Callable, Sequence
  from functools import partial
  import numpy
  import pennylane
  from pennylane.pulse import ParametrizedEvolution
  from pennylane.ops.qubit.special_unitary import pauli_basis_strings, _pauli_decompose
  from pennylane import transform
  from .parameter_shift import _make_zero_rep
  from .pulse_gradient import _assert_has_jax, raise_pulse_diff_on_qnode
  from .gradient_transform import _all_zero_grad, assert_no_state_returns, assert_no_tape_batching, assert_no_variance, choose_trainable_params, find_and_validate_gradient_methods, _no_trainable_grad, reorder_grads
label_desc: Use image node_id 2 for calling a global function with example usage: _one_parameter_paulirot_coeffs(generators, num_wires) and returns: tuple