code: def _standardize_cudnn_weights(
      weights, biases, shape, transpose_weights=False
  ):
      """Utility function convert variable to cuDNN compatible parameter.
  
      Note that Keras weights for kernels are different from the cuDNN format.
      Eg.:
  
      ```
        Keras                 cuDNN
        [[0, 1, 2],  <--->  [[0, 2, 4],
         [3, 4, 5]]          [1, 3, 5]]
      ```
  
      If the input weights need to be in a unified format, then set
      `transpose_weights=True` to convert the weights.
  
      Args:
          weights: list of weights for the kernels and recurrent kernels.
          biases: list of biases for individual gate.
          shape: the shape for the converted variables that will be feed to cuDNN.
          transpose_weights: boolean, whether to transpose the weights.
  
      Returns:
          The converted weights that can be feed to cuDNN ops as param.
      """
  
      def convert(w):
          return tf.transpose(w) if transpose_weights else w
  
      weights = [tf.reshape(convert(x), shape) for x in weights]
      biases = [tf.reshape(x, shape) for x in biases]
      return tf.concat(weights + biases, axis=0)
  
file_path: keras/keras/backend/tensorflow/rnn.py
imports: import tensorflow
  import tree
  from keras.utils.nest import pack_sequence_as
label_desc: Use image node_id 7 for calling a global function with example usage: _standardize_cudnn_weights(weights, biases, shape, transpose_weights) and returns: tf, unknown