tensorflow_probability/python/distributions/von_mises_fisher.py
def _maybe_assert_valid_sample(self, samples):
    """Check counts for proper shape, values, then return tensor version."""
    if not self.validate_args:
      return samples
    with tf.control_dependencies([
        assert_util.assert_near(
            1.,
            tf.linalg.norm(tensor=samples, axis=-1),
            message='samples must be unit length'),
        assert_util.assert_equal(
            tf.shape(input=samples)[-1:],
            self.event_shape_tensor(),
            message=('samples must have innermost dimension matching that of '
                     '`self.mean_direction`')),
    ]):
      return tf.identity(samples)
def _maybe_assert_valid_sample(self, samples):
    """Check counts for proper shape, values, then return tensor version."""
    if not self.validate_args:
      return samples
    with tf.control_dependencies([
        assert_util.assert_near(
            1.,
            tf.linalg.norm(tensor=samples, axis=-1),
            message='samples must be unit length'),
        assert_util.assert_equal(
            tf.shape(input=samples)[-1:],
            self.event_shape_tensor(),
            message=('samples must have innermost dimension matching that of '
                     '`self.mean_direction`')),
    ]):
      return tf.identity(samples)
def _maybe_assert_valid_sample ( self , samples ) : if not self . validate_args : return samples with tf . control_dependencies ( [ assert_util . assert_near ( 1. , tf . linalg . norm ( tensor = samples , axis = - 1 ) , message = 'samples must be unit length' ) , assert_util . assert_equal ( tf . shape ( input = samples ) [ - 1 : ] , self . event_shape_tensor ( ) , message = ( 'samples must have innermost dimension matching that of ' '`self.mean_direction`' ) ) , ] ) : return tf . identity ( samples )
Check counts for proper shape, values, then return tensor version.
Check counts for proper shape values then return tensor version .