You could permute the result tensor as seen here:
conv_out = (F.conv1d(data_reshaped, torch_kernel, padding=padding_value, bias=None) / torch_kernel.shape[2])
ret = conv_out.view(torch_data.shape).permute(0, 2, 1)
print(ret)
> tensor([[[1.0000, 1.0000],
           [2.0000, 2.0000],
           [3.0000, 3.0000],
           [4.0000, 4.0000],
           [5.0000, 5.0000],
           [6.0000, 6.0000],
           [4.3333, 4.3333]],

          [[1.0000, 1.0000],
           [2.0000, 2.0000],
           [3.0000, 3.0000],
           [4.0000, 4.0000],
           [5.0000, 5.0000],
           [6.0000, 6.0000],
           [4.3333, 4.3333]]])

and you could also use a grouped conv approach:
out = F.conv1d(torch_data, torch_kernel.expand(2, -1, -1), padding=padding_value, bias=None, groups=2) / torch_kernel.shape[2]
out = out.permute(0, 2, 1)
print(out)