code: def reshape_feats(feats):
      """Reshapes tensors of shape (batch x channels x features x length)
      to (batch x length x features), suitable for standard SpeechBrain
      losses, such as `mse_loss`
  
      Arguments
      ---------
      feats: torch.Tensor
          a feature tensor of shape (batch x channels x features x length)
  
      Returns
      -------
      result: torch.Tensor
          a feature tensor of shape (batch x length x features)
      """
      return feats.squeeze(1).transpose(1, -1)
  
imports: import logging
  import sys
  import torch
  import speechbrain
  import os
  from collections import namedtuple
  from audiomnist_prepare import prepare_audiomnist
  from hyperpyyaml import load_hyperpyyaml
  from torchaudio import functional
  from speechbrain.dataio.dataio import length_to_mask, write_audio
  from speechbrain.dataio.dataset import apply_overfit_test
  from speechbrain.utils import data_utils
  from speechbrain.utils.train_logger import plot_spectrogram
  from enum import Enum
  from speechbrain.utils.distributed import run_on_main
  from speechbrain.utils.data_utils import dict_value_combinations, dist_stats, masked_mean, masked_min, masked_max, masked_std, match_shape
label_desc: Use image node_id 1 for calling a global function with example usage: reshape_feats(feats) and returns: feats