code: def test_dtype(self):
      # Test int64 indices.
      conv_fn = convolution_util.make_convolution_fn(
          (2, 2),
          rank=2,
          strides=(1, 1),
          padding="SAME",
          dilations=(1, 1),
          dtype=tf.int64,
          validate_args=True,
      )
      x = tf.ones((2, 8, 8, 2), dtype=tf.float32)
      kernel = tf.ones((2, 8, 2), dtype=tf.float32)
      _ = self.evaluate(conv_fn(x, kernel))
  
      # Test f64 input.
      conv_fn = convolution_util.make_convolution_fn(
          self.make_integer_input((2, 2)),
          rank=2,
          strides=self.make_integer_input((1, 1)),
          padding="SAME",
          dilations=self.make_integer_input((1, 1)),
          validate_args=True,
      )
      x = tf.ones((2, 8, 8, 2), dtype=tf.float64)
      kernel = tf.ones((2, 8, 2), dtype=tf.float64)
      y = self.evaluate(conv_fn(x, kernel))
      self.assertDTypeEqual(y, np.float64)
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.experimental.nn.util import convolution_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.internal import tf_keras
label_desc: Use image node_id 4 for calling the _BatchedConvTest obj's underlying member method code with example usage: obj.test_dtype() without return types