code: def _transpose_conv_kernel(kernel):
      # Torch requires conv kernel of format
      # `(out_channels, in_channels, spatial_dims)`, we need to transpose.
      num_spatial_dims = len(kernel.shape) - 2
      if num_spatial_dims == 1:
          kernel = torch.permute(kernel, (2, 1, 0))
      elif num_spatial_dims == 2:
          kernel = torch.permute(kernel, (3, 2, 0, 1))
      elif num_spatial_dims == 3:
          kernel = torch.permute(kernel, (4, 3, 0, 1, 2))
      return kernel
  
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 21 for calling a global function with example usage: _transpose_conv_kernel(kernel) and returns: kernel