Unexpected behavior from `tf.math.greaterEqual`

System information

• Have I written custom code (as opposed to using a stock example script provided in TensorFlow):
  Custom code
• OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
  macOS BigSur 11.1
• TensorFlow installed from (source or binary):
  TensorFlow Java latest GitHub, based on karl/tensor-ttype

I have the following code snippet.

       Operand<TFloat32> r = tf.random.randomUniform(tf.constant(Shape.of(3,2)), TFloat32.class,   
                                                         RandomUniform.seed(1001L));
      Operand<TFloat32> rate1 = tf.constant(0.5f);
      Operand<TBool> mask = tf.math.greaterEqual(r, rate1);

The generated random sequence (shape 3,2) is:

0,0). 0.028465
0,1). 0.936395
1.0). 0.035310
1,1). 0.230565
2,0). 0.269415
2,1). 0.145439

Describe the current behavior
When I run this code, I get mask values of:

0,0). false
0,1). true
1,0). false
1,1). false
2,0). false
2,1). true

Describe the expected behavior
I expected mask values to be:

0,0). false
0,1). true
1,0). false
1,1). false
2,0). false
2,1). false

The last random number, 0.145439, is less than 0.5.

Code to reproduce the issue
Provide a reproducible test case that is the bare minimum necessary to generate the problem.

try (TestSession session = TestSession.createTestSession(TestSession.Mode.GRAPH)) {
      Ops tf = session.getTF();
       Operand<TFloat32> random = tf.random.randomUniform(tf.constant(Shape.of(3,2), TFloat32.class,   
                                                         RandomUniform.seed(1001L));
      Operand<TFloat32> rate = tf.constant(0.5f);
      Operand<TBool> mask = tf.math.greaterEqual(r, rate);

      System.out.println("******** MASK ***************");
      System.out.println("random:");
      session.print(random);
      System.out.println("rate1:");
      session.print(rate1);
      System.out.println("mask:");
      session.print(mask);
      System.out.println("******** MASK ***************");
}

If I do this code in Python it works as expected:

import tensorflow as tf
a = [
    [0.028465 ,  0.936395],
    [0.035310 , 0.230565],
    [0.269415 , 0.145439]
]
print(a);
rate = 0.5
b = tf.math.greater_equal(a, rate);
print(b)

Output:

[[0.028465, 0.936395], [0.03531, 0.230565], [0.269415, 0.145439]]
tf.Tensor(
[[False  True]
 [False False]
 [False False]], shape=(3, 2), dtype=bool)

Start with the Java snippet and the TestSession in GRAPH mode, then compare the generated tensor and tf.math.greaterEqual result with the Python example. Reproduce the reported final-element mismatch and identify whether the Java binding or the test setup produces it; done means the behavior is explained and a regression test or corrective change is verified.