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Computes gradient of the FractionalAvgPool function.

Unlike FractionalMaxPoolGrad, we don't need to find arg_max for FractionalAvgPoolGrad, we just need to evenly back-propagate each element of out_backprop to those indices that form the same pooling cell. Therefore, we just need to know the shape of original input tensor, instead of the whole tensor.

`orig_input_tensor_shape` A `Tensor` of type `int64`. Original input tensor shape for `fractional_avg_pool`
`out_backprop` A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`. 4-D with shape `[batch, height, width, channels]`. Gradients w.r.t. the output of `fractional_avg_pool`.
`row_pooling_sequence` A `Tensor` of type `int64`. row pooling sequence, form pooling region with col_pooling_sequence.
`col_pooling_sequence` A `Tensor` of type `int64`. column pooling sequence, form pooling region with row_pooling sequence.
`overlapping` An optional `bool`. Defaults to `False`. When set to True, it means when pooling, the values at the boundary of adjacent pooling cells are used by both cells. For example:

`index 0 1 2 3 4`

`value 20 5 16 3 7`

If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice. The result would be [41/3, 26/3] for fractional avg pooling.

`name` A name for the operation (optional).

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

[{ "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"label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }]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" }]