text: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  
  class ConvLayer(nn.Module):
      def __init__(self, c_in):
          super(ConvLayer, self).__init__()
          padding = 1 if torch.__version__ >= '1.5.0' else 2
          self.downConv = nn.Conv1d(in_channels=c_in,
                                    out_channels=c_in,
                                    kernel_size=3,
                                    padding=padding,
                                    padding_mode='circular')
          self.norm = nn.BatchNorm1d(c_in)
          self.activation = nn.ELU()
          self.maxPool = nn.MaxPool1d(kernel_size=3, stride=2, padding=1)
  
      def forward(self, x):
          x = self.downConv(x.permute(0, 2, 1))
          x = self.norm(x)
          x = self.activation(x)
          x = self.maxPool(x)
          x = x.transpose(1, 2)
          return x
  
  
  class EncoderLayer(nn.Module):
      def __init__(self, attention, d_model, d_ff=None, dropout=0.1, activation="relu"):
          super(EncoderLayer, self).__init__()
          d_ff = d_ff or 4 * d_model
          self.attention = attention
          self.conv1 = nn.Conv1d(in_channels=d_model, out_channels=d_ff, kernel_size=1)
          self.conv2 = nn.Conv1d(in_channels=d_ff, out_channels=d_model, kernel_size=1)
          self.norm1 = nn.LayerNorm(d_model)
          self.norm2 = nn.LayerNorm(d_model)
          self.dropout = nn.Dropout(dropout)
          self.activation = F.relu if activation == "relu" else F.gelu
  
      def forward(self, x, attn_mask=None):
          # x [B, L, D]
          # x = x + self.dropout(self.attention(
          #     x, x, x,
          #     attn_mask = attn_mask
          # ))
          new_x, attn = self.attention(
              x, x, x,
              attn_mask=attn_mask
          )
          x = x + self.dropout(new_x)
          y = x = self.norm1(x)
          y = self.dropout(self.activation(self.conv1(y.transpose(-1, 1))))
          y = self.dropout(self.conv2(y).transpose(-1, 1))
  
          return self.norm2(x + y), attn
  
  
  class Encoder(nn.Module):
      def __init__(self, attn_layers, conv_layers=None, norm_layer=None):
          super(Encoder, self).__init__()
          self.attn_layers = nn.ModuleList(attn_layers)
          self.conv_layers = nn.ModuleList(conv_layers) if conv_layers is not None else None
          self.norm = norm_layer
  
      def forward(self, x, attn_mask=None):
          # x [B, L, D]
          attns = []
          if self.conv_layers is not None:
              for attn_layer, conv_layer in zip(self.attn_layers, self.conv_layers):
                  x, attn = attn_layer(x, attn_mask=attn_mask)
                  x = conv_layer(x)
                  attns.append(attn)
              x, attn = self.attn_layers[-1](x, attn_mask=attn_mask)
              attns.append(attn)
          else:
              for attn_layer in self.attn_layers:
                  x, attn = attn_layer(x, attn_mask=attn_mask)
                  attns.append(attn)
  
          if self.norm is not None:
              x = self.norm(x)
  
          return x, attns
  
  
  class EncoderStack(nn.Module):
      def __init__(self, encoders, inp_lens):
          super(EncoderStack, self).__init__()
          self.encoders = nn.ModuleList(encoders)
          self.inp_lens = inp_lens
  
      def forward(self, x, attn_mask=None):
          # x [B, L, D]
          x_stack = [];
          attns = []
          for i_len, encoder in zip(self.inp_lens, self.encoders):
              inp_len = x.shape[1] // (2 ** i_len)
              x_s, attn = encoder(x[:, -inp_len:, :])
              x_stack.append(x_s);
              attns.append(attn)
          x_stack = torch.cat(x_stack, -2)
  
          return x_stack, attns
  