class_docstr: Large CNN module proposed for DQN in Nature, 2015.
  
  See: https://www.nature.com/articles/nature14236
class_docstr_tok: ["Large","CNN","module","proposed","for","DQN","in","Nature",",","2015",".","See",":","https",":","\/\/www.nature.com\/articles\/nature14236"]
code: def __init__(
      self,
      n_input_channels=4,
      n_output_channels=512,
      activation=F.relu,
      bias=0.1,
  ):
      self.n_input_channels = n_input_channels
      self.activation = activation
      self.n_output_channels = n_output_channels
      super().__init__()
      self.layers = nn.ModuleList(
          [
              nn.Conv2d(n_input_channels, 32, 8, stride=4),
              nn.Conv2d(32, 64, 4, stride=2),
              nn.Conv2d(64, 64, 3, stride=1),
          ]
      )
      self.output = nn.Linear(3136, n_output_channels)
  
      self.apply(init_chainer_default)
      self.apply(constant_bias_initializer(bias=bias))
  
file_path: pfrl/pfrl/nn/atari_cnn.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from pfrl.initializers import init_chainer_default
label_desc: Use image node_id 1 to create a new LargeAtariCNN object from inherited base classes: nn with example: obj = LargeAtariCNN(n_input_channels, n_output_channels, activation, bias)