class_docstr: Tests the correctness of the Y = X @ X.T transformation.
class_docstr_tok: ["Tests","the","correctness","of","the","Y","=","X","@","X.T","transformation","."]
code: def testNoBatchDeferred(self):
      x_ = np.array([[1.0, 0], [2, 1]])  # np.linalg.cholesky(y)
      y_ = np.array([[1.0, 2], [2, 5]])  # np.matmul(x, x.T)
      x = tf1.placeholder_with_default(x_, shape=None)
      y = tf1.placeholder_with_default(y_, shape=None)
      y_actual = cholesky_outer_product.CholeskyOuterProduct().forward(
          x=x
      )
      x_actual = cholesky_outer_product.CholeskyOuterProduct().inverse(
          y=y
      )
      [y_actual_, x_actual_] = self.evaluate([y_actual, x_actual])
      # Shapes are always known in eager.
      if not tf.executing_eagerly():
          self.assertEqual(None, y_actual.shape)
          self.assertEqual(None, x_actual.shape)
      self.assertAllClose(y_, y_actual_)
      self.assertAllClose(x_, x_actual_)
  
file_path: probability/tensorflow_probability/python/bijectors/cholesky_outer_product_test.py
imports: import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import cholesky_outer_product
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 6 for calling the CholeskyOuterProductBijectorTest obj's underlying member method code with example usage: obj.testNoBatchDeferred() without return types