def blend(image1, image2, factor):
    'Blend image1 and image2 using \'factor\'.\n\n  A value of factor 0.0 means only image1 is used.\n  A value of 1.0 means only image2 is used.  A value between 0.0 and\n  1.0 means we linearly interpolate the pixel values between the two\n  images.  A value greater than 1.0 "extrapolates" the difference\n  between the two pixel values, and we clip the results to values\n  between 0 and 255.\n\n  Args:\n    image1: An image Tensor.\n    image2: An image Tensor.\n    factor: A floating point value above 0.0.\n\n  Returns:\n    A blended image Tensor.\n  '
    image1 = tf.cast(image1, tf.float32)
    image2 = tf.cast(image2, tf.float32)
    return tf.saturate_cast((image1 + (factor * (image2 - image1))), tf.uint8)
Blend image1 and image2 using 'factor'.

A value of factor 0.0 means only image1 is used.
A value of 1.0 means only image2 is used.  A value between 0.0 and
1.0 means we linearly interpolate the pixel values between the two
images.  A value greater than 1.0 "extrapolates" the difference
between the two pixel values, and we clip the results to values
between 0 and 255.

Args:
  image1: An image Tensor.
  image2: An image Tensor.
  factor: A floating point value above 0.0.

Returns:
  A blended image Tensor.
def blend(image1, image2, factor):
    'Blend image1 and image2 using \'factor\'.\n\n  A value of factor 0.0 means only image1 is used.\n  A value of 1.0 means only image2 is used.  A value between 0.0 and\n  1.0 means we linearly interpolate the pixel values between the two\n  images.  A value greater than 1.0 "extrapolates" the difference\n  between the two pixel values, and we clip the results to values\n  between 0 and 255.\n\n  Args:\n    image1: An image Tensor.\n    image2: An image Tensor.\n    factor: A floating point value above 0.0.\n\n  Returns:\n    A blended image Tensor.\n  '
    image1 = tf.cast(image1, tf.float32)
    image2 = tf.cast(image2, tf.float32)
    return tf.saturate_cast((image1 + (factor * (image2 - image1))), tf.uint8)