code: def forward(self, x):
      assert x.ndim == 4
      B, _, T, _ = x.shape
      x = x.view([B, self.H, self.E, T, self.n_freqs])
      x = self.act(x)  # [B,H,E,T,F]
      stat_dim = (2, 4)
      mu_ = x.mean(dim=stat_dim, keepdim=True)  # [B,H,1,T,1]
      std_ = torch.sqrt(
          x.var(dim=stat_dim, unbiased=False, keepdim=True) + self.eps
      )  # [B,H,1,T,1]
      x = ((x - mu_) / std_) * self.gamma + self.beta  # [B,H,E,T,F]
      return x
  
imports: import math
  from collections import OrderedDict
  from typing import Dict, List, Optional, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn import init
  from torch.nn.parameter import Parameter
  from espnet2.enh.decoder.stft_decoder import STFTDecoder
  from espnet2.enh.encoder.stft_encoder import STFTEncoder
  from espnet2.enh.layers.complex_utils import new_complex_like
  from espnet2.enh.separator.abs_separator import AbsSeparator
  from espnet2.torch_utils.get_layer_from_string import get_layer
label_desc: Use image node_id 2 for calling the AllHeadPReLULayerNormalization4DCF obj's underlying member method code with example usage: obj.forward(x) and returns: x