/TensorFlow 2.3

# tf.clip_by_value

Clips tensor values to a specified min and max.

Given a tensor `t`, this operation returns a tensor of the same type and shape as `t` with its values clipped to `clip_value_min` and `clip_value_max`. Any values less than `clip_value_min` are set to `clip_value_min`. Any values greater than `clip_value_max` are set to `clip_value_max`.

Note: `clip_value_min` needs to be smaller or equal to `clip_value_max` for correct results.

#### For example:

Basic usage passes a scalar as the min and max value.

```t = tf.constant([[-10., -1., 0.], [0., 2., 10.]])
t2 = tf.clip_by_value(t, clip_value_min=-1, clip_value_max=1)
t2.numpy()
array([[-1., -1.,  0.],
[ 0.,  1.,  1.]], dtype=float32)
```

The min and max can be the same size as `t`, or broadcastable to that size.

```t = tf.constant([[-1, 0., 10.], [-1, 0, 10]])
clip_min = [[2],[1]]
t3 = tf.clip_by_value(t, clip_value_min=clip_min, clip_value_max=100)
t3.numpy()
array([[ 2.,  2., 10.],
[ 1.,  1., 10.]], dtype=float32)
```

Broadcasting fails, intentionally, if you would expand the dimensions of `t`

```t = tf.constant([[-1, 0., 10.], [-1, 0, 10]])
clip_min = [[[2, 1]]] # Has a third axis
t4 = tf.clip_by_value(t, clip_value_min=clip_min, clip_value_max=100)
Traceback (most recent call last):

InvalidArgumentError: Incompatible shapes: [2,3] vs. [1,1,2]
```

It throws a `TypeError` if you try to clip an `int` to a `float` value (`tf.cast` the input to `float` first).

```t = tf.constant([[1, 2], [3, 4]], dtype=tf.int32)
t5 = tf.clip_by_value(t, clip_value_min=-3.1, clip_value_max=3.1)
Traceback (most recent call last):

TypeError: Cannot convert ...
```
Args
`t` A `Tensor` or `IndexedSlices`.
`clip_value_min` The minimum value to clip to. A scalar `Tensor` or one that is broadcastable to the shape of `t`.
`clip_value_max` The minimum value to clip to. A scalar `Tensor` or one that is broadcastable to the shape of `t`.
`name` A name for the operation (optional).
Returns
A clipped `Tensor` or `IndexedSlices`.
Raises
`tf.errors.InvalidArgumentError`: If the clip tensors would trigger array broadcasting that would make the returned tensor larger than the input.
`TypeError` If dtype of the input is `int32` and dtype of the `clip_value_min` or `clip_value_max` is `float32`