code: def conv_transpose(
      inputs,
      kernel,
      strides=1,
      padding="valid",
      output_padding=None,
      data_format=None,
      dilation_rate=1,
  ):
      inputs = convert_to_tensor(inputs)
      kernel = convert_to_tensor(kernel)
      num_spatial_dims = inputs.ndim - 2
      strides = standardize_tuple(strides, num_spatial_dims, "strides")
  
      data_format = standardize_data_format(data_format)
      (
          torch_padding,
          torch_output_padding,
      ) = compute_conv_transpose_padding_args_for_torch(
          input_shape=inputs.shape,
          kernel_shape=kernel.shape,
          strides=strides,
          padding=padding,
          output_padding=output_padding,
          dilation_rate=dilation_rate,
      )
      if data_format == "channels_last":
          inputs = _transpose_spatial_inputs(inputs)
      # Transpose kernel from keras format to torch format.
      kernel = _transpose_conv_kernel(kernel)
      kernel_spatial_shape = kernel.shape[2:]
      if isinstance(dilation_rate, int):
          dilation_rate = [dilation_rate] * len(kernel_spatial_shape)
  
      if num_spatial_dims == 1:
          outputs = tnn.conv_transpose1d(
              inputs,
              kernel,
              stride=strides,
              padding=torch_padding,
              output_padding=torch_output_padding,
              dilation=dilation_rate,
          )
      elif num_spatial_dims == 2:
          outputs = tnn.conv_transpose2d(
              inputs,
              kernel,
              stride=strides,
              padding=torch_padding,
              output_padding=torch_output_padding,
              dilation=dilation_rate,
          )
      elif num_spatial_dims == 3:
          outputs = tnn.conv_transpose3d(
              inputs,
              kernel,
              stride=strides,
              padding=torch_padding,
              output_padding=torch_output_padding,
              dilation=dilation_rate,
          )
      else:
          raise ValueError(
              "Inputs to conv transpose operation should have ndim=3, 4, or 5,"
              "corresponding to 1D, 2D and 3D inputs. Received input "
              f"shape: {inputs.shape}."
          )
      if data_format == "channels_last":
          outputs = _transpose_spatial_outputs(outputs)
      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 27 for calling a global function with example usage: conv_transpose(inputs, kernel, strides, padding, output_padding, data_format, dilation_rate) and returns: outputs