import torch
from torch.nn.parameter import Parameter


class BasicGraphConvolutionLayer(torch.nn.Module):

    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.W2 = Parameter(torch.rand((in_channels, out_channels), dtype=
            torch.float32))
        self.W1 = Parameter(torch.rand((in_channels, out_channels), dtype=
            torch.float32))
        self.bias = Parameter(torch.zeros(out_channels, dtype=torch.float32))

    def forward(self, X, A):
        potential_msgs = torch.mm(X, self.W2)
        propagated_msgs = torch.mm(A, potential_msgs)
        root_update = torch.mm(X, self.W1)
        output = propagated_msgs + root_update + self.bias
        return output


def get_inputs():
    return [torch.rand([4, 4]), torch.rand([4, 4])]


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_channels': 4}]

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.nn.parameter import Parameter
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_add_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_2, primals_1, out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels._mm_plus_mm(primals_3, buf0, primals_2, primals_4,
            out=buf1)
        del buf0
        del primals_4
        buf2 = buf1
        del buf1
        get_raw_stream(0)
        triton_poi_fused_add_0[grid(16)](buf2, primals_5, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_5
    return buf2, reinterpret_tensor(primals_2, (4, 4), (1, 4), 0
        ), reinterpret_tensor(primals_3, (4, 4), (1, 4), 0)


class BasicGraphConvolutionLayerNew(torch.nn.Module):

    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.W2 = Parameter(torch.rand((in_channels, out_channels), dtype=
            torch.float32))
        self.W1 = Parameter(torch.rand((in_channels, out_channels), dtype=
            torch.float32))
        self.bias = Parameter(torch.zeros(out_channels, dtype=torch.float32))

    def forward(self, input_0, input_1):
        primals_1 = self.W2
        primals_2 = self.W1
        primals_5 = self.bias
        primals_3 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
