code: def get_grad_sample_modes(use_ew: bool = False):
      grad_sample_modes = ["hooks", "functorch"]
      if use_ew and check_torch_version_for_ew_sample():
          grad_sample_modes.append("ew")
      return grad_sample_modes
  
imports: import io
  from typing import Callable, Dict, Iterable, List, Union
  import numpy
  import torch
  import torch.nn
  from opacus.grad_sample import wrap_model
  from opacus.utils.module_utils import trainable_parameters
  from opacus.utils.packed_sequences import compute_seq_lengths
  from torch.nn.utils.rnn import PackedSequence, pad_packed_sequence
label_desc: Use image node_id 6 for calling a global function with example usage: get_grad_sample_modes(use_ew) and returns: grad_sample_modes