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# tf.raw_ops.CumulativeLogsumexp

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

By default, this op performs an inclusive cumulative log-sum-exp, which means that the first element of the input is identical to the first element of the output:

``````tf.math.cumulative_logsumexp([a, b, c])  # => [a, log(exp(a) + exp(b)), log(exp(a) + exp(b) + exp(c))]
``````

By setting the `exclusive` kwarg to `True`, an exclusive cumulative log-sum-exp is performed instead:

``````tf.cumulative_logsumexp([a, b, c], exclusive=True)  # => [-inf, a, log(exp(a) * exp(b))]
``````

Note that the neutral element of the log-sum-exp operation is `-inf`, however, for performance reasons, the minimal value representable by the floating point type is used instead.

By setting the `reverse` kwarg to `True`, the cumulative log-sum-exp is performed in the opposite direction.

`x` A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`. A `Tensor`. Must be one of the following types: `float16`, `float32`, `float64`.
`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 cumulative log-sum-exp.
`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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