code: def __init__(self, input_dimension, eps=1e-5):
      super().__init__()
      assert len(input_dimension) == 3
      H, E, n_freqs = input_dimension
      param_size = [1, H, E, 1, n_freqs]
      self.gamma = Parameter(
          torch.Tensor(*param_size).to(torch.float32)
      )
      self.beta = Parameter(torch.Tensor(*param_size).to(torch.float32))
      init.ones_(self.gamma)
      init.zeros_(self.beta)
      self.act = nn.PReLU(num_parameters=H, init=0.25)
      self.eps = eps
      self.H = H
      self.E = E
      self.n_freqs = n_freqs
  
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 1 to create a new AllHeadPReLULayerNormalization4DCF object from inherited base classes: nn with example: obj = AllHeadPReLULayerNormalization4DCF(input_dimension, eps)