code: def __init__(
      self,
      emb_dim,
      emb_ks,
      emb_hs,
      n_freqs,
      hidden_channels,
      n_head=4,
      approx_qk_dim=512,
      activation="prelu",
      eps=1e-5,
  ):
      super().__init__()
  
      in_channels = emb_dim * emb_ks
  
      self.intra_norm = LayerNormalization4D(emb_dim, eps=eps)
      self.intra_rnn = nn.LSTM(
          in_channels,
          hidden_channels,
          1,
          batch_first=True,
          bidirectional=True,
      )
      self.intra_linear = nn.ConvTranspose1d(
          hidden_channels * 2, emb_dim, emb_ks, stride=emb_hs
      )
  
      self.inter_norm = LayerNormalization4D(emb_dim, eps=eps)
      self.inter_rnn = nn.LSTM(
          in_channels,
          hidden_channels,
          1,
          batch_first=True,
          bidirectional=True,
      )
      self.inter_linear = nn.ConvTranspose1d(
          hidden_channels * 2, emb_dim, emb_ks, stride=emb_hs
      )
  
      E = math.ceil(
          approx_qk_dim * 1.0 / n_freqs
      )  # approx_qk_dim is only approximate
      assert emb_dim % n_head == 0
      for ii in range(n_head):
          self.add_module(
              "attn_conv_Q_%d" % ii,
              nn.Sequential(
                  nn.Conv2d(emb_dim, E, 1),
                  get_layer(activation)(),
                  LayerNormalization4DCF((E, n_freqs), eps=eps),
              ),
          )
          self.add_module(
              "attn_conv_K_%d" % ii,
              nn.Sequential(
                  nn.Conv2d(emb_dim, E, 1),
                  get_layer(activation)(),
                  LayerNormalization4DCF((E, n_freqs), eps=eps),
              ),
          )
          self.add_module(
              "attn_conv_V_%d" % ii,
              nn.Sequential(
                  nn.Conv2d(emb_dim, emb_dim // n_head, 1),
                  get_layer(activation)(),
                  LayerNormalization4DCF(
                      (emb_dim // n_head, n_freqs), eps=eps
                  ),
              ),
          )
      self.add_module(
          "attn_concat_proj",
          nn.Sequential(
              nn.Conv2d(emb_dim, emb_dim, 1),
              get_layer(activation)(),
              LayerNormalization4DCF((emb_dim, n_freqs), eps=eps),
          ),
      )
  
      self.emb_dim = emb_dim
      self.emb_ks = emb_ks
      self.emb_hs = emb_hs
      self.n_head = n_head
  
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 to create a new GridNetBlock object from inherited base classes: nn with example: obj = GridNetBlock(emb_dim, emb_ks, emb_hs, n_freqs, hidden_channels, n_head, approx_qk_dim, activation, eps)