code: def testValuesAreCorrect(self, dtype, batch_size):
      concentration = np.array(5.0, dtype=dtype)
      rate = np.array(0.2, dtype=dtype)
  
      rng = test_util.test_np_rng()
      k = exponential_curve.ExponentialCurve(concentration, rate)
      for _ in range(5):
          x = rng.uniform(0, 2, size=[batch_size, 3]).astype(dtype)
          y = rng.uniform(0, 2, size=[batch_size, 1]).astype(dtype)
          self.assertAllClose(
              self._numpyKernel(
                  concentration,
                  rate,
                  x.sum(-1, keepdims=True),
                  3.0 * y.sum(axis=-1, keepdims=True),
              ),
              self.evaluate(k.apply(x, y)),
          )
  
file_path: probability/tensorflow_probability/python/math/psd_kernels/exponential_curve_test.py
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
  from tensorflow_probability.python.math.psd_kernels import exponential_curve
label_desc: Use image node_id 4 for calling the ExponentialCurveTest obj's underlying member method code with example usage: obj.testValuesAreCorrect(dtype, batch_size) without return types