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 (possibly nested) structure of the spaces that `inputs`
              belongs to.
          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::
          :skipif: True
  
          # B=2
          from ray.rllib.utils.tf_utils import flatten_inputs_to_1d_tensor
          from gymnasium.spaces import Discrete, Box
          out = flatten_inputs_to_1d_tensor(
              {"a": [1, 0], "b": [[[0.0], [0.1]], [1.0], [1.1]]},
              spaces_struct=dict(a=Discrete(2), b=Box(shape=(2, 1)))
          )
          print(out)
  
          # B=2; T=2
          out = flatten_inputs_to_1d_tensor(
              ([[1, 0], [0, 1]],
               [[[0.0, 0.1], [1.0, 1.1]], [[2.0, 2.1], [3.0, 3.1]]]),
              spaces_struct=tuple([Discrete(2), Box(shape=(2, ))]),
              time_axis=True
          )
          print(out)
  
      .. testoutput::
  
          [[0.0, 1.0,  0.0, 0.1], [1.0, 0.0,  1.0, 1.1]]  # B=2 n=4
          [[[0.0, 1.0, 0.0, 0.1], [1.0, 0.0, 1.0, 1.1]],
          [[1.0, 0.0, 2.0, 2.1], [0.0, 1.0, 3.0, 3.1]]]  # B=2 T=2 n=4
      """
  
      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):
          assert isinstance(input_, np.ndarray)
  
          # 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_ = np.reshape(input_, [B * T])
              out.append(
                  one_hot(input_, depth=space.n).astype(np.float32)
              )
          # Multi one-hot encoding.
          elif isinstance(space, MultiDiscrete):
              if time_axis:
                  input_ = np.reshape(input_, [B * T, -1])
              out.append(
                  np.concatenate(
                      [
                          one_hot(input_[:, i], depth=n).astype(
                              np.float32
                          )
                          for i, n in enumerate(space.nvec)
                      ],
                      axis=-1,
                  )
              )
          # Box: Flatten.
          else:
              if time_axis:
                  input_ = np.reshape(input_, [B * T, -1])
              else:
                  input_ = np.reshape(input_, [B, -1])
              out.append(input_.astype(np.float32))
  
      merged = np.concatenate(out, axis=-1)
      # Restore the time-dimension, if applicable.
      if time_axis:
          merged = np.reshape(merged, [B, T, -1])
  
      return merged
  
imports: from collections import OrderedDict
  from gymnasium.spaces import Discrete, MultiDiscrete
  import numpy
  import tree
  from types import MappingProxyType
  from typing import List, Optional
  from ray.rllib.utils.annotations import PublicAPI
  from ray.rllib.utils.deprecation import Deprecated
  from ray.rllib.utils.framework import try_import_tf, try_import_torch
  from ray.rllib.utils.typing import SpaceStruct, TensorType, TensorStructType, Union
label_desc: Use image node_id 5 for calling a global function with example usage: flatten_inputs_to_1d_tensor(inputs, spaces_struct, time_axis) and returns: merged