code: def _filter_top_k(x, k):
      """Filters top-k values in the last dim of x and set the rest to NEG_INF.
  
      Used for computing top-k prediction values in dense labels (which has the same
      shape as predictions) for recall and precision top-k metrics.
  
      Args:
        x: tensor with any dimensions.
        k: the number of values to keep.
  
      Returns:
        tensor with same shape and dtype as x.
      """
      _, top_k_idx = nn_ops.top_k(x, k, sorted=False)
      top_k_mask = math_ops.reduce_sum(
          array_ops.one_hot(top_k_idx, array_ops.shape(x)[-1], axis=-1),
          axis=-2,
      )
      return x * top_k_mask + NEG_INF * (1 - top_k_mask)
  
imports: from enum import Enum
  import functools
  import weakref
  import numpy
  from tensorflow.python.compat import compat
  from tensorflow.python.distribute import distribute_lib
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor
  from tensorflow.python.framework import tensor_conversion
  from tensorflow.python.keras import backend
  from tensorflow.python.keras.utils import losses_utils
  from tensorflow.python.keras.utils import tf_utils
  from tensorflow.python.keras.utils.generic_utils import to_list
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import array_ops_stack
  from tensorflow.python.ops import check_ops
  from tensorflow.python.ops import clip_ops
  from tensorflow.python.ops import control_flow_ops
  from tensorflow.python.ops import gen_math_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import nn_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.ops import weights_broadcast_ops
  from tensorflow.python.ops.parallel_for import control_flow_ops
  from tensorflow.python.ops.ragged import ragged_tensor
  from tensorflow.python.util import tf_decorator
label_desc: Use image node_id 9 for calling a global function with example usage: _filter_top_k(x, k) and returns: unknown