code: def compute_accidental_hits(
      true_classes, sampled_candidates, num_true, seed=None, name=None
  ):
      """Compute the position ids in `sampled_candidates` matching `true_classes`.
  
      In Candidate Sampling, this operation facilitates virtually removing
      sampled classes which happen to match target classes.  This is done
      in Sampled Softmax and Sampled Logistic.
  
      See our [Candidate Sampling Algorithms
      Reference](http://www.tensorflow.org/extras/candidate_sampling.pdf).
  
      We presuppose that the `sampled_candidates` are unique.
  
      We call it an 'accidental hit' when one of the target classes
      matches one of the sampled classes.  This operation reports
      accidental hits as triples `(index, id, weight)`, where `index`
      represents the row number in `true_classes`, `id` represents the
      position in `sampled_candidates`, and weight is `-FLOAT_MAX`.
  
      The result of this op should be passed through a `sparse_to_dense`
      operation, then added to the logits of the sampled classes. This
      removes the contradictory effect of accidentally sampling the true
      target classes as noise classes for the same example.
  
      Args:
        true_classes: A `Tensor` of type `int64` and shape `[batch_size,
          num_true]`. The target classes.
        sampled_candidates: A tensor of type `int64` and shape `[num_sampled]`.
          The sampled_candidates output of CandidateSampler.
        num_true: An `int`.  The number of target classes per training example.
        seed: An `int`. An operation-specific seed. Default is 0.
        name: A name for the operation (optional).
  
      Returns:
        indices: A `Tensor` of type `int32` and shape `[num_accidental_hits]`.
          Values indicate rows in `true_classes`.
        ids: A `Tensor` of type `int64` and shape `[num_accidental_hits]`.
          Values indicate positions in `sampled_candidates`.
        weights: A `Tensor` of type `float` and shape `[num_accidental_hits]`.
          Each value is `-FLOAT_MAX`.
  
      """
      seed1, seed2 = random_seed.get_seed(seed)
      return gen_candidate_sampling_ops.compute_accidental_hits(
          true_classes,
          sampled_candidates,
          num_true,
          seed=seed1,
          seed2=seed2,
          name=name,
      )
  
file_path: tensorflow/tensorflow/python/ops/candidate_sampling_ops.py
imports: from tensorflow.python.framework import random_seed
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gen_candidate_sampling_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.util import deprecation
  from tensorflow.python.util import dispatch
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 6 for calling a global function with example usage: compute_accidental_hits(true_classes, sampled_candidates, num_true, seed, name) and returns: gen_candidate_sampling_ops