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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.