conv1d and conv2d should support further batching

It looks like conv1d and conv2d expect only one dimension of batching (NxCxI to conv1d). In torch you can do additional dimensions of batching (N1xN2xCxI to conv1d), compare:

import torch
import torch.nn.functional as F
x = torch.zeros(3,1,4,4, dtype=torch.float32)
y = torch.zeros(3,1,4,4, dtype=torch.float64);
F.conv1d(x,y)

and

let x = dsharp.zeros([3;1;4;4])
let y = dsharp.zeros([3;1;4;4])
dsharp.conv1d(x,y)

gives

System.Exception: Expecting two 3d Tensors t1, t2 where t1 is input (NxCxI: batchSize x inputChannels x inputLength) and t2 is filters (KxCxF: outputChannels x inputChannels x kernelLength), received Tensors with shapes [|3; 1; 4; 4|], [|3; 1; 4; 4|]

[moloneymb]

Supporting ‘further batching’ should be a byproduct of implementing a reshape operation. The reshape operation should not add significant overhead as it just changes the shape.

[gbaydin]

Closing this. Please ping me if there is something I missed and we can reopen.