code: def tokenize(
      tokenizer: Encoder, inputs: dict, framework: str
  ) -> dict:
      """Tokenizes the observations from the input dict.
  
      Args:
          tokenizer: The tokenizer to use.
          inputs: The input dict.
  
      Returns:
          The output dict.
      """
      # Tokenizer may depend solely on observations.
      obs = inputs[SampleBatch.OBS]
      tokenizer_inputs = {SampleBatch.OBS: obs}
      size = list(obs.size() if framework == "torch" else obs.shape)
      b_dim, t_dim = size[:2]
      fold, unfold = get_fold_unfold_fns(
          b_dim, t_dim, framework=framework
      )
      # Push through the tokenizer encoder.
      out = tokenizer(fold(tokenizer_inputs))
      out = out[ENCODER_OUT]
      # Then unfold batch- and time-dimensions again.
      return unfold(out)
  
imports: import abc
  from typing import List, Optional, Tuple, Union
  from ray.rllib.core.models.configs import ModelConfig
  from ray.rllib.core.models.specs.specs_base import Spec
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.rnn_sequencing import get_fold_unfold_fns
  from ray.rllib.utils.annotations import ExperimentalAPI, DeveloperAPI
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.typing import TensorType
label_desc: Use image node_id 1 for calling a global function with example usage: tokenize(tokenizer, inputs, framework) and returns: unfold