code: def to_dict(self):
      d = {
          "action": F.one_hot(
              torch.from_numpy(np.stack(self.action)), 2
          )
      }
      for f in [
          "observation",
          "reward",
          "terminal",
          "log_prob",
          "possible_actions_mask",
      ]:
          if self.optional_field_exist.get(f, True):
              f_value = getattr(self, f)
              if np.isscalar(f_value[0]):
                  # scalar values
                  d[f] = torch.tensor(f_value)
              else:
                  # vector values, need to stack
                  d[f] = torch.from_numpy(np.stack(f_value)).float()
      return d
  
imports: from abc import ABC, abstractmethod
  from dataclasses import asdict, dataclass, field, fields
  from typing import Any, Callable, Dict, List, Optional, Union
  import numpy
  import reagent.core.types
  import torch
  import torch.nn.functional
label_desc: Use image node_id 6 for calling the Trajectory obj's underlying member method code with example usage: obj.to_dict() and returns: d