class_docstr: Neural Network to solve Atari problem.
  
  Args:
      act_dim (int): Dimension of action space.
      dueling (bool): True if use dueling architecture else False
code: def __init__(self, act_dim, dueling=False):
      super().__init__()
      self.conv1 = nn.Conv2d(
          in_channels=4,
          out_channels=32,
          kernel_size=5,
          stride=1,
          padding=2,
      )
      self.conv2 = nn.Conv2d(
          in_channels=32,
          out_channels=32,
          kernel_size=5,
          stride=1,
          padding=2,
      )
      self.conv3 = nn.Conv2d(
          in_channels=32,
          out_channels=64,
          kernel_size=4,
          stride=1,
          padding=1,
      )
      self.conv4 = nn.Conv2d(
          in_channels=64,
          out_channels=64,
          kernel_size=3,
          stride=1,
          padding=1,
      )
      self.relu = nn.ReLU()
      self.max_pool = nn.MaxPool2d(kernel_size=2, stride=2)
      self.flatten = nn.Flatten()
  
      self.dueling = dueling
  
      if dueling:
          self.linear_1_adv = nn.Linear(
              in_features=6400, out_features=512
          )
          self.linear_2_adv = nn.Linear(
              in_features=512, out_features=act_dim
          )
          self.linear_1_val = nn.Linear(
              in_features=6400, out_features=512
          )
          self.linear_2_val = nn.Linear(in_features=512, out_features=1)
  
      else:
          self.linear_1 = nn.Linear(
              in_features=6400, out_features=act_dim
          )
  
      self.reset_params()
  
imports: import torch.nn
  import parl
label_desc: Use image node_id 1 to create a new AtariModel object from inherited base classes: parl with example: obj = AtariModel(act_dim, dueling)