If I have a 1D data set of size `1`

by `D`

and want to apply a 1D convolution of kernel size `K`

and the number of filters is `F`

, how does one do it?

```
import torch
D = 10 # size of the data
x=torch.nn.FloatTensor(1,D)
```

essentially what I want to do is apply the same function (represented by the weight sharing) to each data point of `x(d)`

(meaning it has kernel size `K=1`

for this example of course). So at the end of the convolution, I want to add all the values of the filters for a specific point in x to produce `f(x(d))`

. So each convolution will have `F`

filters and those `F`

filters are linearly combined to produce the output of the function applied at each `x(d)`

.

Example: