code: def flatten_inputs_to_1d_tensor(
      inputs: TensorStructType,
      spaces_struct: Optional[SpaceStruct] = None,
      time_axis: bool = False,
  ) -> TensorType:
      """Flattens arbitrary input structs according to the given spaces struct.
  
      Returns a single 1D tensor resulting from the different input
      components' values.
  
      Thereby:
      - Boxes (any shape) get flattened to (B, [T]?, -1). Note that image boxes
      are not treated differently from other types of Boxes and get
      flattened as well.
      - Discrete (int) values are one-hot'd, e.g. a batch of [1, 0, 3] (B=3 with
      Discrete(4) space) results in [[0, 1, 0, 0], [1, 0, 0, 0], [0, 0, 0, 1]].
      - MultiDiscrete values are multi-one-hot'd, e.g. a batch of
      [[0, 2], [1, 4]] (B=2 with MultiDiscrete([2, 5]) space) results in
      [[1, 0,  0, 0, 1, 0, 0], [0, 1,  0, 0, 0, 0, 1]].
  
      Args:
          inputs: The inputs to be flattened.
          spaces_struct: The structure of the spaces that behind the input
          time_axis: Whether all inputs have a time-axis (after the batch axis).
              If True, will keep not only the batch axis (0th), but the time axis
              (1st) as-is and flatten everything from the 2nd axis up.
  
      Returns:
          A single 1D tensor resulting from concatenating all
          flattened/one-hot'd input components. Depending on the time_axis flag,
          the shape is (B, n) or (B, T, n).
  
      .. testcode::
  
          from gymnasium.spaces import Discrete, Box
          from ray.rllib.utils.torch_utils import flatten_inputs_to_1d_tensor
          import torch
          struct = {
              "a": np.array([1, 3]),
              "b": (
                  np.array([[1.0, 2.0], [4.0, 5.0]]),
                  np.array(
                      [[[8.0], [7.0]], [[5.0], [4.0]]]
                  ),
              ),
                  "c": {
                      "cb": np.array([1.0, 2.0]),
                  },
          }
          struct_torch = tree.map_structure(lambda s: torch.from_numpy(s), struct)
          spaces = dict(
              {
                  "a": gym.spaces.Discrete(4),
                  "b": (gym.spaces.Box(-1.0, 10.0, (2,)), gym.spaces.Box(-1.0, 1.0, (2,
                          1))),
                  "c": dict(
                      {
                          "cb": gym.spaces.Box(-1.0, 1.0, ()),
                      }
                  ),
              }
          )
          print(flatten_inputs_to_1d_tensor(struct_torch, spaces_struct=spaces))
  
      .. testoutput::
  
          tensor([[0., 1., 0., 0., 1., 2., 8., 7., 1.],
                  [0., 0., 0., 1., 4., 5., 5., 4., 2.]])
  
      """
  
      flat_inputs = tree.flatten(inputs)
      flat_spaces = (
          tree.flatten(spaces_struct)
          if spaces_struct is not None
          else [None] * len(flat_inputs)
      )
  
      B = None
      T = None
      out = []
      for input_, space in zip(flat_inputs, flat_spaces):
          # Store batch and (if applicable) time dimension.
          if B is None:
              B = input_.shape[0]
              if time_axis:
                  T = input_.shape[1]
  
          # One-hot encoding.
          if isinstance(space, Discrete):
              if time_axis:
                  input_ = torch.reshape(input_, [B * T])
              out.append(one_hot(input_, space).float())
          # Multi one-hot encoding.
          elif isinstance(space, MultiDiscrete):
              if time_axis:
                  input_ = torch.reshape(input_, [B * T, -1])
              out.append(one_hot(input_, space).float())
          # Box: Flatten.
          else:
              if time_axis:
                  input_ = torch.reshape(input_, [B * T, -1])
              else:
                  input_ = torch.reshape(input_, [B, -1])
              out.append(input_.float())
  
      merged = torch.cat(out, dim=-1)
      # Restore the time-dimension, if applicable.
      if time_axis:
          merged = torch.reshape(merged, [B, T, -1])
  
      return merged
  
imports: import logging
  import os
  import warnings
  from typing import Dict, List, Optional, TYPE_CHECKING, Union
  import gymnasium
  import numpy
  import tree
  from gymnasium.spaces import Discrete, MultiDiscrete
  from packaging import version
  import ray
  from ray.rllib.models.repeated_values import RepeatedValues
  from ray.rllib.utils.annotations import Deprecated, PublicAPI, DeveloperAPI
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.numpy import SMALL_NUMBER
  from ray.rllib.utils.typing import LocalOptimizer, SpaceStruct, TensorStructType, TensorType
label_desc: Use image node_id 9 for calling a global function with example usage: flatten_inputs_to_1d_tensor(inputs, spaces_struct, time_axis) and returns: merged