code: def conv_transpose(
      inputs,
      kernel,
      strides=1,
      padding="valid",
      output_padding=None,
      data_format=None,
      dilation_rate=1,
  ):
      data_format = standardize_data_format(data_format)
      tf_data_format = _convert_data_format(
          data_format, len(inputs.shape)
      )
      kernel_size = kernel.shape[:-2]
      filters = kernel.shape[-2]
      input_shape = list(inputs.shape)
      symbolic_shape = tf.shape(inputs)
      for i, e in enumerate(input_shape):
          if e is None:
              input_shape[i] = symbolic_shape[i]
      output_shape = compute_conv_transpose_output_shape(
          input_shape,
          kernel_size,
          filters,
          strides,
          padding,
          output_padding,
          data_format,
          dilation_rate,
      )
  
      return tf.nn.conv_transpose(
          inputs,
          kernel,
          output_shape,
          strides,
          padding=padding.upper(),
          data_format=tf_data_format,
          dilations=dilation_rate,
      )
  
imports: import warnings
  import tensorflow
  from keras.backend import standardize_data_format
  from keras.backend import standardize_dtype
  from keras.backend.common.backend_utils import compute_conv_transpose_output_shape
  from keras.backend.config import epsilon
  from keras.backend.tensorflow.core import cast
  from keras.backend.tensorflow.core import convert_to_tensor
label_desc: Use image node_id 25 for calling a global function with example usage: conv_transpose(inputs, kernel, strides, padding, output_padding, data_format, dilation_rate) and returns: tf