code: 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
  
imports: import math
  import numpy
  from typing import Optional
  import torch
  import torch.nn
  from neuralforecast.common._modules import TransEncoderLayer, TransEncoder, TransDecoderLayer, TransDecoder, DataEmbedding, AttentionLayer
  from ..common._base_windows import BaseWindows
  from ..losses.pytorch import MAE
label_desc: Use image node_id 2 for calling the ConvLayer obj's underlying member method code with example usage: obj.forward(x) and returns: x