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tfp.experimental.distributions.marginal_fns.ps.maximum

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Returns the max of x and y (i.e. x > y ? x : y) element-wise.

Example:

````x = tf.constant([0., 0., 0., 0.])`
`y = tf.constant([-2., 0., 2., 5.])`
`tf.math.maximum(x, y)`
`<tf.Tensor: shape=(4,), dtype=float32, numpy=array([0., 0., 2., 5.], dtype=float32)>`
```

Note that `maximum` supports broadcast semantics for `x` and `y`.

````x = tf.constant([-5., 0., 0., 0.])`
`y = tf.constant([-3.])`
`tf.math.maximum(x, y)`
`<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-3., 0., 0., 0.], dtype=float32)>`
```

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

`x` A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `int8`, `uint8`, `int16`, `uint16`, `int32`, `uint32`, `int64`, `uint64`.
`y` A `Tensor`. Must have the same type as `x`.
`name` A name for the operation (optional).

A `Tensor`. Has the same type as `x`.

[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Missing the information I need" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Easy to understand" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Solved my problem" },{ "type": "thumb-up", "id": "otherUp", "label":"Other" }]