code: def _nonzero_coeffs_and_words(coefficients, num_wires, atol=1e-8):
      """Given a tuple of coefficient tensors with a common first axis size :math:`4^N-1`,
      where :math:`N` is the number of wires, filter out those indices for the first axis for which
      the sliced coefficients are not all zero. Return these coefficients, as well as the
      corresponding Pauli word strings on :math:`N` wires.
  
      Args:
          coefficients(tuple[tensor_like]): Coefficients to filter.
          num_wires (int): Number of qubits the generators act on.
          atol (float): absolute tolerance used to determine whether a tensor is zero.
      """
      words = pauli_basis_strings(num_wires)
      new_coefficients = [[] for _ in coefficients]
      new_words = []
      for word, *coeffs in zip(words, *coefficients):
          if all(
              qml.math.allclose(c, 0.0, atol=atol, rtol=0.0)
              for c in coeffs
          ):
              continue
          new_words.append(word)
          for new_coeffs, c in zip(new_coefficients, coeffs):
              new_coeffs.append(c)
      return new_coefficients, new_words
  
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 3 for calling a global function with example usage: _nonzero_coeffs_and_words(coefficients, num_wires, atol) and returns: new_coefficients,  new_words