code: def top_k(x, k, sorted=False):
      sorted_indices = np.argsort(x, axis=-1)[..., ::-1]
      sorted_values = np.sort(x, axis=-1)[..., ::-1]
  
      if sorted:
          # Take the k largest values.
          top_k_values = sorted_values[..., :k]
          top_k_indices = sorted_indices[..., :k]
      else:
          # Partition the array such that all values larger than the k-th
          # largest value are to the right of it.
          top_k_values = np.partition(x, -k, axis=-1)[..., -k:]
          top_k_indices = np.argpartition(x, -k, axis=-1)[..., -k:]
  
          # Get the indices in sorted order.
          idx = np.argsort(-top_k_values, axis=-1)
  
          # Get the top k values and their indices.
          top_k_values = np.take_along_axis(top_k_values, idx, axis=-1)
          top_k_indices = np.take_along_axis(
              top_k_indices, idx, axis=-1
          )
  
      return top_k_values, top_k_indices
  
imports: import numpy
  from keras.backend import standardize_dtype
  from keras.backend.jax.math import fft
  from keras.backend.jax.math import fft2
  from keras.backend.numpy.core import convert_to_tensor
  from keras.utils.module_utils import scipy
label_desc: Use image node_id 3 for calling a global function with example usage: top_k(x, k, sorted) and returns: top_k_values,  top_k_indices