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# tf.raw_ops.Cumprod

Compute the cumulative product of the tensor `x` along `axis`.

By default, this op performs an inclusive cumprod, which means that the first element of the input is identical to the first element of the output:

``````tf.cumprod([a, b, c])  # => [a, a * b, a * b * c]
``````

By setting the `exclusive` kwarg to `True`, an exclusive cumprod is performed instead:

``````tf.cumprod([a, b, c], exclusive=True)  # => [1, a, a * b]
``````

By setting the `reverse` kwarg to `True`, the cumprod is performed in the opposite direction:

``````tf.cumprod([a, b, c], reverse=True)  # => [a * b * c, b * c, c]
``````

This is more efficient than using separate `tf.reverse` ops.

The `reverse` and `exclusive` kwargs can also be combined:

``````tf.cumprod([a, b, c], exclusive=True, reverse=True)  # => [b * c, c, 1]
``````

`x` A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. A `Tensor`. Must be one of the following types: `float32`, `float64`, `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`, `complex128`, `qint8`, `quint8`, `qint32`, `half`.
`axis` A `Tensor`. Must be one of the following types: `int32`, `int64`. A `Tensor` of type `int32` (default: 0). Must be in the range `[-rank(x), rank(x))`.
`exclusive` An optional `bool`. Defaults to `False`. If `True`, perform exclusive cumprod.
`reverse` An optional `bool`. Defaults to `False`. A `bool` (default: False).
`name` A name for the operation (optional).

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

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