Yes Mr. @ptrblck, in_channels was missing and the weights implementation was wrong but now it worked as this:
model.features[0].weight = nn.Parameter(data= torch.tensor([
                        [[[1, 2, 1], [0, 0, 0], [-1, -2, -1]],
                         [[1, 2, 1], [0, 0, 0], [-1, -2, -1]],   
                         [[1, 2, 1], [0, 0, 0], [-1, -2, -1]]], 
                                          ###############  end of filter n°1
                        [[[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]],
                         [[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]],
                         [[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]],
                                            ############## end of filter n°2
                         [[[2, 1, 0], [1, 0, -1], [0, -1, -2]],
                          [[2, 1, 0], [1, 0, -1], [0, -1, -2]],
                          [[2, 1, 0], [1, 0, -1], [0, -1, -2]]],
                                            ############## end of filter n°3
                         [[[0, -1, -2], [1, 0, -1], [2, 1, 0]],
                          [[0, -1, -2], [1, 0, -1], [2, 1, 0]],
                          [[0, -1, -2], [1, 0, -1], [2, 1, 0]]]],  dtype= torch.float32), requires_grad= True)

I just encountered a new error since I changed Linear layer :

mat1 dim 1 must match mat2 dim 0

If you would please to suggest any solution that may work it would be appreciated