Tensor-aware control flow

When working with TensorFlow tensors, avoid using Python comparison operators (<, >=, ==) or conditional checks that expect Boolean scalars. Python operators evaluate eagerly and won’t work correctly with tensors that represent deferred computations.

Instead:

1. Use TensorFlow’s conditional operations like tf.cond
2. Use comparison functions from check_ops module such as assert_less
3. Use TensorFlow’s mathematical operations for comparisons

Incorrect:

def random_uniform(shape, minval=0, maxval=None, dtype=dtypes.float32):
  if minval >= maxval:  # Will fail if these are tensors
    raise ValueError("minval must be less than maxval")

Correct:

def random_uniform(shape, minval=0, maxval=None, dtype=dtypes.float32):
  # Use check_ops for tensor-compatible validation
  minval = ops.convert_to_tensor(minval, dtype=dtype)
  maxval = ops.convert_to_tensor(maxval, dtype=dtype)
  check_ops.assert_less(minval, maxval, 
                        message="minval must be less than maxval")

This pattern is essential for building TensorFlow graphs that will execute correctly in both eager and graph execution modes, ensuring your AI models behave as expected regardless of execution environment.