class_docstr: Computes Kendall's Tau-c Rank Correlation Coefficient.
  
  Usage:
  >>> actuals = tf.constant([12, 2, 1, 12, 2], dtype=tf.int32)
  >>> preds = tf.constant([1, 4, 7, 1, 0], dtype=tf.int32)
  >>> m = tfa.metrics.KendallsTauC(0, 13, 0, 8)
  >>> m.update_state(actuals, preds)
  >>> m.result().numpy()
  -0.48000002
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code: def result(self):
      p, q, _, _ = self._compute_variables()
      n = tf.cast(self.n, tf.float32)
      non_zeros_col = tf.math.count_nonzero(self.ncol)
      non_zeros_row = tf.math.count_nonzero(self.nrow)
      m = tf.cast(tf.minimum(non_zeros_col, non_zeros_row), tf.float32)
      return 2 * (p - q) / (tf.square(n) * (m - 1) / m)
  
imports: from abc import abstractmethod
  import numpy
  import tensorflow
  from tensorflow.keras import backend
  from tensorflow.keras.metrics import Metric
  from tensorflow_addons.utils.types import AcceptableDTypes
  from typeguard import typechecked
label_desc: Use image node_id 1 for calling the KendallsTauC obj's underlying member method code with example usage: obj.result() and returns: unknown