code: def test_dtype(self):
      # Test int64 indices.
      conv_fn = self.make_conv_fn(
          (2, 2), strides=1, padding="SAME", dilations=1
      )
      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 = self.make_conv_fn(
          (2, 2), strides=1, padding="SAME", dilations=1
      )
      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 5 for calling the _BatchedConvTransposeTest obj's underlying member method code with example usage: obj.test_dtype() without return types