code: def __init__(
      self,
      tensor_length: int,
      input_size: int,
      scalars_size: int,
      emb_dim: int,
      n_steps: int,
      n_logits: int,
      n_samples: int,
  ):
      # scalar_size = s
      # input_size = S
      # tensor_length = T
      # emb_dim = c
      super().__init__()
      self.tensor_length = tensor_length
      self.input_size = input_size
      self.emb_dim = emb_dim
      self.torso = TorsoModel(
          scalars_size, input_size, tensor_length, emb_dim
      )
      emb_size = 3 * input_size * input_size
      print("Build policy head")
      self.policy_head = PolicyHead(
          emb_size, emb_dim, n_steps, n_logits, n_samples
      )
      print("Build value head")
      self.value_head = ValueHead(
          2048
      )  # value dependent on num_head and proj_dim
      self.policy_loss_fn = torch.nn.CrossEntropyLoss(reduction="sum")
      self.quantile_loss_fn = QuantileLoss()
      self.risk_value_management = ValueRiskManagement()
  
file_path: nebuly/optimization/open_alpha_tensor/open_alpha_tensor/core/modules/alpha_tensor.py
imports: import torch
  from open_alpha_tensor.core.modules.extras import QuantileLoss, ValueRiskManagement
  from open_alpha_tensor.core.modules.heads import PolicyHead, ValueHead
  from open_alpha_tensor.core.modules.torso import TorsoModel
label_desc: Use image node_id 1 to create a new AlphaTensorModel object from inherited base classes: torch.nn with example: obj = AlphaTensorModel(tensor_length, input_size, scalars_size, emb_dim, n_steps, n_logits, n_samples)