class_docstr: Test the abstract base class behaviors.
class_docstr_tok: ["Test","the","abstract","base","class","behaviors","."]
code: def createKernelInputs(self, batched=False):
      x = tf1.placeholder_with_default(
          np.float32(
              [[1.0, 1.0, 1.0], [2.0, 2.0, 2.0], [3.0, 3.0, 3.0]]
          ),
          shape=[3, 3],
      )
      y = tf1.placeholder_with_default(
          np.float32(
              [[4.0, 4.0, 4.0], [5.0, 5.0, 5.0], [6.0, 6.0, 6.0]]
          ),
          shape=[3, 3],
      )
      z = tf1.placeholder_with_default(
          np.float32(
              [
                  [4.0, 4.0, 4.0],
                  [5.0, 5.0, 5.0],
                  [6.0, 6.0, 6.0],
                  [7.0, 7.0, 7.0],
              ]
          ),
          shape=[4, 3],
      )
      if not batched:
          return x, y, z
  
      x = tf.stack([x] * 5)
      y = tf.stack([y] * 5)
      z = tf.stack([z] * 5)
  
      return x, y, z
  
imports: import functools
  import operator
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math.psd_kernels import positive_semidefinite_kernel
  from tensorflow_probability.python.math.psd_kernels.internal import test_util
  from tensorflow_probability.python.math.psd_kernels.internal import util
label_desc: Use image node_id 1 for calling the PositiveSemidefiniteKernelTest obj's underlying member method code with example usage: obj.createKernelInputs(batched) and returns: x,  y,  z, x,  y,  z