code: def testCDFWithBatchAndFloatDtype(self):
      histograms = [
          [0.1, 0.2, 0.3, 0.2, 0.2],
          [0.1, 0.2, 0.3, 0.2, 0.2],
          [0.1, 0.2, 0.3, 0.2, 0.2],
          [0.1, 0.2, 0.3, 0.2, 0.2],
      ]
      # Note we're testing events outside [0, K-1].
      event = [-1.0, 10.0, 2.0, 2.5]
      expected_cdf = [0.0, 1.0, 0.6, 0.6]
      dist = categorical.Categorical(
          probs=histograms, dtype=tf.float32, validate_args=False
      )
      cdf_op = dist.cdf(event)
  
      self.assertAllClose(expected_cdf, self.evaluate(cdf_op))
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.distributions import categorical
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
  from tensorflow_probability.python.util import deferred_tensor
label_desc: Use image node_id 11 for calling the CategoricalTest obj's underlying member method code with example usage: obj.testCDFWithBatchAndFloatDtype() without return types