code: def __init__(
      self,
      *,
      price_mean: float,
      price_std: float,
      volume_mean: float,
      volume_std: float,
      y_std: float,
      y_mean: float,
      activation: nn.Module,
      conv_sizes: List[Tuple[int, int]],
      dropout=0.1,
  ):
      super().__init__()
  
      self.y_mean = y_mean
      self.y_std = y_std
      self.volume_std = volume_std
      self.volume_mean = volume_mean
      self.price_std = price_std
      self.price_mean = price_mean
      layers = []
      in_channels = CandleIdx.all
      for s in conv_sizes:
          layers.append(
              FrontPaddedConv1d(
                  in_channels=in_channels,
                  out_channels=s[0],
                  kernel_size=s[1],
              )
          )
          in_channels = s[0]
      self.layers = nn.ModuleList(layers)
  
      self.final = FrontPaddedConv1d(
          in_channels=in_channels, out_channels=1, kernel_size=1
      )
      self.activation = activation
  
      self.conv_dropout = nn.Dropout(dropout)
  
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 1 to create a new CnnModel object from inherited base classes: Module with example: obj = CnnModel()