code: def __call__(self, x: torch.Tensor):
      x = x.permute((0, 2, 1))
      x[:, 0 : CandleIdx.prices, :] = (
          x[:, 0 : CandleIdx.prices, :] - self.price_mean
      ) / self.price_std
      x[:, CandleIdx.volume, :] = (
          x[:, CandleIdx.prices, :] - self.volume_mean
      ) / self.volume_std
      for layer in self.layers:
          x = self.activation(self.conv_dropout(layer(x)))
      x = self.final(x)
  
      x = x.permute((0, 2, 1))
      return x
  
file_path: labml/samples/stocks/model.py
imports: from typing import List, Tuple
  import torch
  from labml_helpers.module import Module
  from torch import nn
  from stocks import CandleIdx
label_desc: Use image node_id 2 for calling the CnnModel obj's underlying member method code with example usage: obj.__call__(x) and returns: x