code: def __init__(
      self,
      obs_space,
      action_space,
      num_outputs: int,
      model_config: ModelConfigDict,
      name: str,
  ):
      TorchModelV2.__init__(
          self, obs_space, action_space, num_outputs, model_config, name
      )
      nn.Module.__init__(self)
      try:
          self.preprocessor = get_preprocessor(
              obs_space.original_space
          )(obs_space.original_space)
      except Exception:
          self.preprocessor = get_preprocessor(obs_space)(obs_space)
  
      self.action_masking = False
      self.alpha_zero_obs = True
      if self.alpha_zero_obs:
          self.input_channel_size = 111
      else:
          self.input_channel_size = 19
  
      filters = 32
      res_blocks = 3
      se_channels = 0
      policy_conv_size = 73
      policy_output_size = 4672
      self.num_outputs = 4672
      self.name = name
      self.obs_space = obs_space
      self.action_space = action_space
      self.model_config = model_config
  
      self.filters = filters
      self.res_blocks = res_blocks
      self.se_channels = se_channels
      self.policy_conv_size = policy_conv_size
      self.policy_output_size = policy_output_size
      self.pre_conv = nn.Conv2d(
          self.input_channel_size, self.filters, 3, padding="same"
      )
      self.conv1 = nn.Conv2d(
          self.filters, self.filters, 3, padding="same"
      )
      self.conv2 = nn.Conv2d(
          self.filters, self.filters, 3, padding="same"
      )
      self.pool = nn.AvgPool2d(8)
      self.se1 = nn.Linear(self.filters, self.se_channels)
      self.se2 = nn.Linear(self.se_channels, self.filters * 2)
      self.fc_head = nn.Linear(self.filters * 64, 128)
      self.value_head = nn.Linear(128, 1)
      self.policy_conv1 = nn.Conv2d(
          self.filters, self.policy_conv_size, 3, padding="same"
      )
      self.policy_fc = nn.Linear(
          self.policy_conv_size * 64, self.policy_output_size
      )
      self._value = None
  
file_path: ray/rllib_contrib/leela_chess_zero/src/rllib_leela_chess_zero/leela_chess_zero/leela_chess_zero_model.py
imports: import numpy
  from ray.rllib.models.preprocessors import get_preprocessor
  from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.typing import ModelConfigDict, TensorType
label_desc: Use image node_id 1 to create a new LeelaChessZeroModel object from inherited base classes: TorchModelV2, nn with example: obj = LeelaChessZeroModel(obs_space, action_space, num_outputs, model_config, name)