code: def normalize_batch(x, seq_len, normalize_type):
      x_mean = None
      x_std = None
      if normalize_type == "per_feature":
          x_mean = torch.zeros(
              (seq_len.shape[0], x.shape[1]),
              dtype=x.dtype,
              device=x.device,
          )
          x_std = torch.zeros(
              (seq_len.shape[0], x.shape[1]),
              dtype=x.dtype,
              device=x.device,
          )
          for i in range(x.shape[0]):
              if x[i, :, : seq_len[i]].shape[1] == 1:
                  raise ValueError(
                      "normalize_batch with `per_feature` normalize_type received a tensor of length 1. This will result "
                      "in torch.std() returning nan. Make sure your audio length has enough samples for a single "
                      "feature (ex. at least `hop_length` for Mel Spectrograms)."
                  )
              x_mean[i, :] = x[i, :, : seq_len[i]].mean(dim=1)
              x_std[i, :] = x[i, :, : seq_len[i]].std(dim=1)
          # make sure x_std is not zero
          x_std += CONSTANT
          return (
              (x - x_mean.unsqueeze(2)) / x_std.unsqueeze(2),
              x_mean,
              x_std,
          )
      elif normalize_type == "all_features":
          x_mean = torch.zeros(
              seq_len.shape, dtype=x.dtype, device=x.device
          )
          x_std = torch.zeros(
              seq_len.shape, dtype=x.dtype, device=x.device
          )
          for i in range(x.shape[0]):
              x_mean[i] = x[i, :, : seq_len[i].item()].mean()
              x_std[i] = x[i, :, : seq_len[i].item()].std()
          # make sure x_std is not zero
          x_std += CONSTANT
          return (
              (x - x_mean.view(-1, 1, 1)) / x_std.view(-1, 1, 1),
              x_mean,
              x_std,
          )
      elif (
          "fixed_mean" in normalize_type
          and "fixed_std" in normalize_type
      ):
          x_mean = torch.tensor(
              normalize_type["fixed_mean"], device=x.device
          )
          x_std = torch.tensor(
              normalize_type["fixed_std"], device=x.device
          )
          return (
              (x - x_mean.view(x.shape[0], x.shape[1]).unsqueeze(2))
              / x_std.view(x.shape[0], x.shape[1]).unsqueeze(2),
              x_mean,
              x_std,
          )
      else:
          return x, x_mean, x_std
  
imports: import math
  import random
  from typing import Optional, Tuple, Union
  import librosa
  import numpy
  import torch
  import torch.nn
  from nemo.collections.asr.parts.preprocessing.perturb import AudioAugmentor
  from nemo.collections.asr.parts.preprocessing.segment import AudioSegment
  from nemo.utils import logging
label_desc: Use image node_id 1 for calling a global function with example usage: normalize_batch(x, seq_len, normalize_type) and returns: unknown,  x_mean,  x_std, unknown,  x_mean,  x_std, unknown,  x_mean,  x_std, x,  x_mean,  x_std