code: def _make_one_hot(ints, num_categories):
      x = np.zeros((ints.shape[0], num_categories))
      x[np.arange(ints.shape[0]), ints] = 1
      return x.astype(np.float32)
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.experimental.psd_kernels import feature_scaled_with_categorical
  from tensorflow_probability.python.experimental.psd_kernels import feature_scaled_with_embedded_categorical
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
  from tensorflow_probability.python.math.psd_kernels import exponentiated_quadratic
  from tensorflow_probability.python.math.psd_kernels import feature_scaled
  from tensorflow_probability.python.math.psd_kernels import matern
label_desc: Use image node_id 2 for calling a global function with example usage: _make_one_hot(ints, num_categories) and returns: x