code: def _generate_input_dict(B, T, obs_space, action_space):
      """Generate input_dict that has completely fake values."""
      # generate deterministic inputs
      # obs
      obs = np.arange(
          B * T * obs_space.shape[0], dtype=np.float32
      ).reshape((B, T, obs_space.shape[0]))
      # actions
      if isinstance(action_space, gym.spaces.Box):
          act = np.arange(
              B * T * action_space.shape[0], dtype=np.float32
          ).reshape((B, T, action_space.shape[0]))
      else:
          act = np.mod(
              np.arange(B * T, dtype=np.int32).reshape((B, T)),
              action_space.n,
          )
      # returns to go
      rtg = np.arange(B * (T + 1), dtype=np.float32).reshape(
          (B, T + 1, 1)
      )
      # timesteps
      timesteps = np.stack(
          [np.arange(T, dtype=np.int32) for _ in range(B)], axis=0
      )
      # attention mask
      mask = np.ones((B, T), dtype=np.float32)
  
      input_dict = SampleBatch(
          {
              SampleBatch.OBS: obs,
              SampleBatch.ACTIONS: act,
              SampleBatch.RETURNS_TO_GO: rtg,
              SampleBatch.T: timesteps,
              SampleBatch.ATTENTION_MASKS: mask,
          }
      )
      input_dict = convert_to_torch_tensor(input_dict)
      return input_dict
  
file_path: ray/rllib_contrib/dt/tests/test_dt_model.py
imports: import unittest
  import gymnasium
  import numpy
  from rllib_dt.dt.dt_torch_model import DTTorchModel
  import ray
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils.framework import try_import_tf, try_import_torch
  from ray.rllib.utils.numpy import convert_to_numpy
  from ray.rllib.utils.torch_utils import convert_to_torch_tensor
label_desc: Use image node_id 3 for calling a global function with example usage: _generate_input_dict(B, T, obs_space, action_space) and returns: input_dict