code: def auto_fold_unfold_time(input_spec: str):
      """Automatically folds/unfolds the time dimension of a tensor.
  
      This is useful when calling the model requires a batch dimension only, but the
      input data has a batch- and a time-dimension. This decorator will automatically
      fold the time dimension into the batch dimension before calling the model and
      unfold the batch dimension back into the time dimension after calling the model.
  
      Args:
          input_spec: The input spec of the model.
  
      Returns:
          A decorator that automatically folds/unfolds the time_dimension if present.
      """
  
      def decorator(func):
          @functools.wraps(func)
          def wrapper(self, input_data, **kwargs):
              if not hasattr(self, input_spec):
                  raise ValueError(
                      "The model must have an input_specs attribute to "
                      "automatically fold/unfold the time dimension."
                  )
              if not torch.is_tensor(input_data):
                  raise ValueError(
                      f"input_data must be a torch.Tensor to fold/unfold "
                      f"time automatically, but got {type(input_data)}."
                  )
              # Attempt to fold/unfold the time dimension.
              actual_shape = list(input_data.shape)
              spec = getattr(self, input_spec)
  
              try:
                  # Validate the input data against the input spec to find out it we
                  # should attempt to fold/unfold the time dimension.
                  spec.validate(input_data)
              except ValueError as original_error:
                  # Attempt to fold/unfold the time dimension.
                  # Calculate a new shape for the input data.
                  b, t = actual_shape[:2]
                  other_dims = actual_shape[2:]
                  reshaped_b = b * t
                  new_shape = tuple([reshaped_b] + other_dims)
                  reshaped_inputs = input_data.reshape(new_shape)
                  try:
                      spec.validate(reshaped_inputs)
                  except ValueError as new_error:
                      raise SpecCheckingError(
                          f"Attempted to call {func} with input data of shape "
                          f"{actual_shape}. RLlib attempts to automatically fold/unfold "
                          f"the time dimension because {actual_shape} does not match the "
                          f"input spec {spec}. In an attempt to fold the time "
                          f"dimensions to possibly fit the input specs of {func}, "
                          f"RLlib has calculated the new shape {new_shape} and "
                          f"reshaped the input data to {reshaped_inputs}. However, "
                          f"the input data still does not match the input spec. "
                          f"\nOriginal error: \n{original_error}. \nNew error:"
                          f" \n{new_error}."
                      )
                  # Call the actual wrapped function
                  outputs = func(self, reshaped_inputs, **kwargs)
                  # Attempt to unfold the time dimension.
                  return outputs.reshape(
                      (b, t) + tuple(outputs.shape[1:])
                  )
              # If above we could validate the spec, we can call the actual wrapped
              # function.
              return func(self, input_data, **kwargs)
  
          return wrapper
  
      return decorator
  
imports: import functools
  from typing import Optional
  import numpy
  from ray.rllib.core.models.base import Model
  from ray.rllib.core.models.configs import CNNTransposeHeadConfig, FreeLogStdMLPHeadConfig, MLPHeadConfig
  from ray.rllib.core.models.specs.checker import SpecCheckingError
  from ray.rllib.core.models.specs.specs_base import Spec
  from ray.rllib.core.models.specs.specs_base import TensorSpec
  from ray.rllib.core.models.torch.base import TorchModel
  from ray.rllib.core.models.torch.primitives import TorchCNNTranspose, TorchMLP
  from ray.rllib.models.utils import get_initializer_fn
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.framework import try_import_torch
label_desc: Use image node_id 1 for calling a global function with example usage: auto_fold_unfold_time(input_spec) and returns: decorator, wrapper, func, outputs