Fixed#938: Speed up njit-decorated function for sliding dot product [WIP]

[NimaSarajpoor]

This PR addresses the issue raised in #938. We want to create a new function for sliding_dot_product that:
(1) has a performance that is close to the performance of core.sliding_dot_product
(2) can be called by a njit-decorated function

Currently, the existing alternative to core.sliding_dot_product is core._sliding_dot_product; however, in contrast to core.sliding_dot_product, its performance is NOT independent of m, the length of query.

As suggested by @seanlaw, we are going to try OTFFT to implement fft and ifft. Then, we use them to create a new function for sliding_dot_product.

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All modified and coverable lines are covered by tests ✅

Comparison is base (857063c) 98.93% compared to head (2c5dd4d) 98.93%.
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##             main     #939   +/-   ##
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  Coverage   98.93%   98.93%           
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  Files          84       84           
  Lines       14292    14292           
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  Hits        14140    14140           
  Misses        152      152           

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[NimaSarajpoor]

@seanlaw
I think the previous notebook got too messy and there were different versions there. I did not remove it (I just kept it for our future reference). Instead, I just pushed a new notebook (see c2a9b43). My goal is to add functions step by step based on the discussions we have had so far on this PR and #938.

For now I just resolved the previously- provided comments (by you and me) that were regarding the initial notebook. I will consider them in this new notebook though.

[NimaSarajpoor]

Line #20.        x[:] = scipy.fft.fft(x)  # we will implement our fft shortly!

We will later work on implementing this fft function using 6-step / 8-step algorithm. We then change this line and the caller function accordingly.

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