# tf.raw_ops.LogicalOr

Returns the truth value of x OR y element-wise.

Logical OR function.

Requires that `x` and `y` have the same shape or have broadcast-compatible shapes. For example, `x` and `y` can be:

• Two single elements of type `bool`.
• One `tf.Tensor` of type `bool` and one single `bool`, where the result will be calculated by applying logical OR with the single element to each element in the larger Tensor.
• Two `tf.Tensor` objects of type `bool` of the same shape. In this case, the result will be the element-wise logical OR of the two input tensors.

You can also use the `|` operator instead.

#### Usage:

````a = tf.constant([True])`
`b = tf.constant([False])`
`tf.math.logical_or(a, b)`
`<tf.Tensor: shape=(1,), dtype=bool, numpy=array([ True])>`
`a | b`
`<tf.Tensor: shape=(1,), dtype=bool, numpy=array([ True])>`
```
````c = tf.constant([False])`
`x = tf.constant([False, True, True, False])`
`tf.math.logical_or(c, x)`
`<tf.Tensor: shape=(4,), dtype=bool, numpy=array([False, True,  True, False])>`
`c | x`
`<tf.Tensor: shape=(4,), dtype=bool, numpy=array([False, True,  True, False])>`
```
````y = tf.constant([False, False, True, True])`
`z = tf.constant([False, True, False, True])`
`tf.math.logical_or(y, z)`
`<tf.Tensor: shape=(4,), dtype=bool, numpy=array([False, True, True, True])>`
`y | z`
`<tf.Tensor: shape=(4,), dtype=bool, numpy=array([False, True, True, True])>`
```

````tf.logical_or([[True, False]], [[True], [False]])`
`<tf.Tensor: shape=(2, 2), dtype=bool, numpy=`
`array([[ True,  True],`
`     [ True, False]])>`
```

The reduction version of this elementwise operation is `tf.math.reduce_any`.

`x` A `tf.Tensor` of type bool.
`y` A `tf.Tensor` of type bool.
`name` A name for the operation (optional).

A `tf.Tensor` of type bool with the shape that `x` and `y` broadcast to.

`x` A `Tensor` of type `bool`.
`y` A `Tensor` of type `bool`.
`name` A name for the operation (optional).

A `Tensor` of type `bool`.

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