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Returns image gradients (dy, dx) for each color channel.

Both output tensors have the same shape as the input: [batch_size, h, w, d]. The gradient values are organized so that [I(x+1, y) - I(x, y)] is in location (x, y). That means that dy will always have zeros in the last row, and dx will always have zeros in the last column.

`image` Tensor with shape [batch_size, h, w, d].

Pair of tensors (dy, dx) holding the vertical and horizontal image gradients (1-step finite difference).

#### Usage Example:

``````BATCH_SIZE = 1
IMAGE_HEIGHT = 5
IMAGE_WIDTH = 5
CHANNELS = 1
image = tf.reshape(tf.range(IMAGE_HEIGHT * IMAGE_WIDTH * CHANNELS,
delta=1, dtype=tf.float32),
shape=(BATCH_SIZE, IMAGE_HEIGHT, IMAGE_WIDTH, CHANNELS))
print(image[0, :,:,0])
tf.Tensor(
[[ 0.  1.  2.  3.  4.]
[ 5.  6.  7.  8.  9.]
[10. 11. 12. 13. 14.]
[15. 16. 17. 18. 19.]
[20. 21. 22. 23. 24.]], shape=(5, 5), dtype=float32)
print(dx[0, :,:,0])
tf.Tensor(
[[5. 5. 5. 5. 5.]
[5. 5. 5. 5. 5.]
[5. 5. 5. 5. 5.]
[5. 5. 5. 5. 5.]
[0. 0. 0. 0. 0.]], shape=(5, 5), dtype=float32)
print(dy[0, :,:,0])
tf.Tensor(
[[1. 1. 1. 1. 0.]
[1. 1. 1. 1. 0.]
[1. 1. 1. 1. 0.]
[1. 1. 1. 1. 0.]
[1. 1. 1. 1. 0.]], shape=(5, 5), dtype=float32)
``````

`ValueError` If `image` is not a 4D tensor.

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