code: def _transpose_spatial_outputs(outputs):
      # Undo the tranpose in `_transpose_spatial_inputs`.
      num_spatial_dims = len(outputs.shape) - 2
      if num_spatial_dims == 1:
          outputs = torch.permute(outputs, (0, 2, 1))
      elif num_spatial_dims == 2:
          outputs = torch.permute(outputs, (0, 2, 3, 1))
      elif num_spatial_dims == 3:
          outputs = torch.permute(outputs, (0, 2, 3, 4, 1))
      return outputs
  
imports: import torch
  import torch.nn.functional
  import tree
  from keras.backend import standardize_data_format
  from keras.backend import standardize_dtype
  from keras.backend.common.backend_utils import compute_conv_transpose_padding_args_for_torch
  from keras.backend.config import epsilon
  from keras.backend.torch.core import cast
  from keras.backend.torch.core import convert_to_tensor
  from keras.backend.torch.core import get_device
  from keras.backend.torch.numpy import expand_dims
  from keras.backend.torch.numpy import maximum
  from keras.backend.torch.numpy import where
  from keras.utils.argument_validation import standardize_tuple
label_desc: Use image node_id 20 for calling a global function with example usage: _transpose_spatial_outputs(outputs) and returns: outputs