/TensorFlow 2.4

# tf.where

Return the elements where `condition` is `True` (multiplexing `x` and `y`).

This operator has two modes: in one mode both `x` and `y` are provided, in another mode neither are provided. `condition` is always expected to be a `tf.Tensor` of type `bool`.

#### Retrieving indices of `True` elements

If `x` and `y` are not provided (both are None):

`tf.where` will return the indices of `condition` that are `True`, in the form of a 2-D tensor with shape (n, d). (Where n is the number of matching indices in `condition`, and d is the number of dimensions in `condition`).

Indices are output in row-major order.

```tf.where([True, False, False, True])
<tf.Tensor: shape=(2, 1), dtype=int64, numpy=
array([[0],
[3]])>
```
```tf.where([[True, False], [False, True]])
<tf.Tensor: shape=(2, 2), dtype=int64, numpy=
array([[0, 0],
[1, 1]])>
```
```tf.where([[[True, False], [False, True], [True, True]]])
<tf.Tensor: shape=(4, 3), dtype=int64, numpy=
array([[0, 0, 0],
[0, 1, 1],
[0, 2, 0],
[0, 2, 1]])>
```

#### Multiplexing between `x` and `y`

If `x` and `y` are provided (both have non-None values):

`tf.where` will choose an output shape from the shapes of `condition`, `x`, and `y` that all three shapes are broadcastable to.

The `condition` tensor acts as a mask that chooses whether the corresponding element / row in the output should be taken from `x` (if the element in `condition` is True) or `y` (if it is false).

```tf.where([True, False, False, True], [1,2,3,4], [100,200,300,400])
<tf.Tensor: shape=(4,), dtype=int32, numpy=array([  1, 200, 300,   4],
dtype=int32)>
tf.where([True, False, False, True], [1,2,3,4], [100])
<tf.Tensor: shape=(4,), dtype=int32, numpy=array([  1, 100, 100,   4],
dtype=int32)>
tf.where([True, False, False, True], [1,2,3,4], 100)
<tf.Tensor: shape=(4,), dtype=int32, numpy=array([  1, 100, 100,   4],
dtype=int32)>
tf.where([True, False, False, True], 1, 100)
<tf.Tensor: shape=(4,), dtype=int32, numpy=array([  1, 100, 100,   1],
dtype=int32)>
```
```tf.where(True, [1,2,3,4], 100)
<tf.Tensor: shape=(4,), dtype=int32, numpy=array([1, 2, 3, 4],
dtype=int32)>
tf.where(False, [1,2,3,4], 100)
<tf.Tensor: shape=(4,), dtype=int32, numpy=array([100, 100, 100, 100],
dtype=int32)>
```

Note that if the gradient of either branch of the tf.where generates a NaN, then the gradient of the entire tf.where will be NaN. A workaround is to use an inner tf.where to ensure the function has no asymptote, and to avoid computing a value whose gradient is NaN by replacing dangerous inputs with safe inputs.

```y = tf.constant(-1, dtype=tf.float32)
tf.where(y > 0, tf.sqrt(y), y)
<tf.Tensor: shape=(), dtype=float32, numpy=-1.0>
```

Use this

```tf.where(y > 0, tf.sqrt(tf.where(y > 0, y, 1)), y)
<tf.Tensor: shape=(), dtype=float32, numpy=-1.0>
```
Args
`condition` A `tf.Tensor` of type `bool`
`x` If provided, a Tensor which is of the same type as `y`, and has a shape broadcastable with `condition` and `y`.
`y` If provided, a Tensor which is of the same type as `x`, and has a shape broadcastable with `condition` and `x`.
`name` A name of the operation (optional).
Returns
If `x` and `y` are provided: A `Tensor` with the same type as `x` and `y`, and shape that is broadcast from `condition`, `x`, and `y`. Otherwise, a `Tensor` with shape `(num_true, dim_size(condition))`.
Raises
`ValueError` When exactly one of `x` or `y` is non-None, or the shapes are not all broadcastable.