code: def compute_priors_and_value(self, obs):
      new_obs = torch.from_numpy(
          obs["observation"]
          .astype(np.float32)
          .reshape([1, 8, 8, self.input_channel_size])
      )
      new_action_mask = torch.from_numpy(
          obs["action_mask"]
          .astype(np.float32)
          .reshape([1, self.num_outputs])
      )
      input_dict = {
          "obs": {
              "observation": new_obs,
              "action_mask": new_action_mask,
          }
      }
      with torch.no_grad():
          model_out = self.forward(input_dict, None, [1])
          logits, _ = model_out
          value = self.value_function()
          logits, value = torch.squeeze(logits), torch.squeeze(value)
          priors = nn.Softmax(dim=-1)(logits)
          value = nn.Tanh()(value)
  
          priors = priors.cpu().numpy()
          value = value.cpu().numpy()
          return priors, value
  
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 6 for calling the LeelaChessZeroModel obj's underlying member method code with example usage: obj.compute_priors_and_value(obs) and returns: priors,  value