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::
          :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):
          input_ = tf.convert_to_tensor(input_)
          shape = tf.shape(input_)
          # Store batch and (if applicable) time dimension.
          if B is None:
              B = shape[0]
              if time_axis:
                  T = shape[1]
  
          # One-hot encoding.
          if isinstance(space, Discrete):
              if time_axis:
                  input_ = tf.reshape(input_, [B * T])
              out.append(tf.cast(one_hot(input_, space), tf.float32))
          elif isinstance(space, MultiDiscrete):
              if time_axis:
                  input_ = tf.reshape(input_, [B * T, -1])
              out.append(tf.cast(one_hot(input_, space), tf.float32))
          # Flatten.
          else:
              if time_axis:
                  input_ = tf.reshape(input_, [B * T, -1])
              else:
                  input_ = tf.reshape(input_, [B, -1])
              out.append(tf.cast(input_, tf.float32))
  
      merged = tf.concat(out, axis=-1)
      # Restore the time-dimension, if applicable.
      if time_axis:
          merged = tf.reshape(merged, [B, T, -1])
  
      return merged
  
imports: import logging
  from typing import Any, Callable, List, Optional, Type, TYPE_CHECKING, Union
  import gymnasium
  import numpy
  import tree
  from gymnasium.spaces import Discrete, MultiDiscrete
  from ray.rllib.utils.annotations import PublicAPI, DeveloperAPI
  from ray.rllib.utils.framework import try_import_tf
  from ray.rllib.utils.spaces.space_utils import get_base_struct_from_space
  from ray.rllib.utils.typing import LocalOptimizer, ModelGradients, PartialAlgorithmConfigDict, SpaceStruct, TensorStructType, TensorType
label_desc: Use image node_id 3 for calling a global function with example usage: flatten_inputs_to_1d_tensor(inputs, spaces_struct, time_axis) and returns: merged