# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_4/inductor_cache/h7/ch7gznsrcg4y4o5d2ifvetpxhe6c5ieteeuh5azx66ij3nbrggv7.py
# Topologically Sorted Source Nodes: [x_out_1, mul_1, x_out_2, mul_2, x_out_3, mul_3, x_out_4, mul_4, x_out_5, mul_5, x_out_6, mul_6, x_out_7, mul_7, x_out_8, mul_8, x_out_9, mul_9, x_out_10, mul_10, x_out_11, mul_11, x_out_12, mul_12, x_out_13, mul_13, x_out_14, mul_14, x_out_15, mul_15, x_out_16, x_out_17, mul_16, x_out_18], Original ATen: [aten.add, aten.mul, aten.div]
# Source node to ATen node mapping:
#   mul_1 => mul_1
#   mul_10 => mul_10
#   mul_11 => mul_11
#   mul_12 => mul_12
#   mul_13 => mul_13
#   mul_14 => mul_14
#   mul_15 => mul_15
#   mul_16 => mul_16
#   mul_2 => mul_2
#   mul_3 => mul_3
#   mul_4 => mul_4
#   mul_5 => mul_5
#   mul_6 => mul_6
#   mul_7 => mul_7
#   mul_8 => mul_8
#   mul_9 => mul_9
#   x_out_1 => mul
#   x_out_10 => add_9
#   x_out_11 => add_10
#   x_out_12 => add_11
#   x_out_13 => add_12
#   x_out_14 => add_13
#   x_out_15 => add_14
#   x_out_16 => add_15
#   x_out_17 => div
#   x_out_18 => add_16
#   x_out_2 => add_1
#   x_out_3 => add_2
#   x_out_4 => add_3
#   x_out_5 => add_4
#   x_out_6 => add_5
#   x_out_7 => add_6
#   x_out_8 => add_7
#   x_out_9 => add_8
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm, 0.9), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm_1, 0.9), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm_2, 0.9), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_1, %mul_2), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm_3, 0.9), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_2, %mul_3), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm_4, 0.9), kwargs = {})
#   %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_3, %mul_4), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm_5, 0.9), kwargs = {})
#   %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_4, %mul_5), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm_6, 0.9), kwargs = {})
#   %add_6 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_5, %mul_6), kwargs = {})
#   %mul_7 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm_7, 0.9), kwargs = {})
#   %add_7 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_6, %mul_7), kwargs = {})
#   %mul_8 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm_8, 0.9), kwargs = {})
#   %add_8 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_7, %mul_8), kwargs = {})
#   %mul_9 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm_9, 0.9), kwargs = {})
#   %add_9 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_8, %mul_9), kwargs = {})
#   %mul_10 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm_10, 0.9), kwargs = {})
#   %add_10 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_9, %mul_10), kwargs = {})
#   %mul_11 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm_11, 0.9), kwargs = {})
#   %add_11 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_10, %mul_11), kwargs = {})
#   %mul_12 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm_12, 0.9), kwargs = {})
#   %add_12 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_11, %mul_12), kwargs = {})
#   %mul_13 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm_13, 0.9), kwargs = {})
#   %add_13 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_12, %mul_13), kwargs = {})
#   %mul_14 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm_14, 0.9), kwargs = {})
#   %add_14 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_13, %mul_14), kwargs = {})
#   %mul_15 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm_15, 0.9), kwargs = {})
#   %add_15 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_14, %mul_15), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add_15, 16), kwargs = {})
#   %mul_16 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, 0.1), kwargs = {})
#   %add_16 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%div, %mul_16), kwargs = {})
triton_poi_fused_add_div_mul_0 = async_compile.triton('triton_poi_fused_add_div_mul_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: '*fp32', 9: '*fp32', 10: '*fp32', 11: '*fp32', 12: '*fp32', 13: '*fp32', 14: '*fp32', 15: '*fp32', 16: '*fp32', 17: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_div_mul_0', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 17, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_div_mul_0(in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, in_ptr8, in_ptr9, in_ptr10, in_ptr11, in_ptr12, in_ptr13, in_ptr14, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
    tmp3 = tl.load(in_ptr0 + (x0), xmask)
    tmp6 = tl.load(in_ptr1 + (x0), xmask)
    tmp9 = tl.load(in_ptr2 + (x0), xmask)
    tmp12 = tl.load(in_ptr3 + (x0), xmask)
    tmp15 = tl.load(in_ptr4 + (x0), xmask)
    tmp18 = tl.load(in_ptr5 + (x0), xmask)
    tmp21 = tl.load(in_ptr6 + (x0), xmask)
    tmp24 = tl.load(in_ptr7 + (x0), xmask)
    tmp27 = tl.load(in_out_ptr1 + (x0), xmask)
    tmp30 = tl.load(in_ptr8 + (x0), xmask)
    tmp33 = tl.load(in_ptr9 + (x0), xmask)
    tmp36 = tl.load(in_ptr10 + (x0), xmask)
    tmp39 = tl.load(in_ptr11 + (x0), xmask)
    tmp42 = tl.load(in_ptr12 + (x0), xmask)
    tmp45 = tl.load(in_ptr13 + (x0), xmask)
    tmp50 = tl.load(in_ptr14 + (x0), xmask)
    tmp1 = 0.9
    tmp2 = tmp0 * tmp1
    tmp4 = tmp3 * tmp1
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp1
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp1
    tmp11 = tmp8 + tmp10
    tmp13 = tmp12 * tmp1
    tmp14 = tmp11 + tmp13
    tmp16 = tmp15 * tmp1
    tmp17 = tmp14 + tmp16
    tmp19 = tmp18 * tmp1
    tmp20 = tmp17 + tmp19
    tmp22 = tmp21 * tmp1
    tmp23 = tmp20 + tmp22
    tmp25 = tmp24 * tmp1
    tmp26 = tmp23 + tmp25
    tmp28 = tmp27 * tmp1
    tmp29 = tmp26 + tmp28
    tmp31 = tmp30 * tmp1
    tmp32 = tmp29 + tmp31
    tmp34 = tmp33 * tmp1
    tmp35 = tmp32 + tmp34
    tmp37 = tmp36 * tmp1
    tmp38 = tmp35 + tmp37
    tmp40 = tmp39 * tmp1
    tmp41 = tmp38 + tmp40
    tmp43 = tmp42 * tmp1
    tmp44 = tmp41 + tmp43
    tmp46 = tmp45 * tmp1
    tmp47 = tmp44 + tmp46
    tmp48 = 0.0625
    tmp49 = tmp47 * tmp48
    tmp51 = 0.1
    tmp52 = tmp50 * tmp51
    tmp53 = tmp49 + tmp52
    tl.store(in_out_ptr1 + (x0), tmp53, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    assert_size_stride(arg1_1, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.mm]
        extern_kernels.mm(arg1_1, arg0_1, out=buf0)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.mm]
        extern_kernels.mm(arg1_1, buf0, out=buf1)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.mm]
        extern_kernels.mm(arg1_1, buf1, out=buf2)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.mm]
        extern_kernels.mm(arg1_1, buf2, out=buf3)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.mm]
        extern_kernels.mm(arg1_1, buf3, out=buf4)
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.mm]
        extern_kernels.mm(arg1_1, buf4, out=buf5)
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_6], Original ATen: [aten.mm]
        extern_kernels.mm(arg1_1, buf5, out=buf6)
        buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_7], Original ATen: [aten.mm]
        extern_kernels.mm(arg1_1, buf6, out=buf7)
        buf8 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_8], Original ATen: [aten.mm]
        extern_kernels.mm(arg1_1, buf7, out=buf8)
        buf10 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_9], Original ATen: [aten.mm]
        extern_kernels.mm(arg1_1, buf8, out=buf10)
        buf11 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_10], Original ATen: [aten.mm]
        extern_kernels.mm(arg1_1, buf10, out=buf11)
        buf12 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_11], Original ATen: [aten.mm]
        extern_kernels.mm(arg1_1, buf11, out=buf12)
        buf13 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_12], Original ATen: [aten.mm]
        extern_kernels.mm(arg1_1, buf12, out=buf13)
        buf14 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_13], Original ATen: [aten.mm]
        extern_kernels.mm(arg1_1, buf13, out=buf14)
        buf15 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_14], Original ATen: [aten.mm]
        extern_kernels.mm(arg1_1, buf14, out=buf15)
        buf16 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_15], Original ATen: [aten.mm]
        extern_kernels.mm(arg1_1, buf15, out=buf16)
        del arg1_1
        buf9 = buf0; del buf0  # reuse
        buf17 = buf10; del buf10  # reuse
        # Topologically Sorted Source Nodes: [x_out_1, mul_1, x_out_2, mul_2, x_out_3, mul_3, x_out_4, mul_4, x_out_5, mul_5, x_out_6, mul_6, x_out_7, mul_7, x_out_8, mul_8, x_out_9, mul_9, x_out_10, mul_10, x_out_11, mul_11, x_out_12, mul_12, x_out_13, mul_13, x_out_14, mul_14, x_out_15, mul_15, x_out_16, x_out_17, mul_16, x_out_18], Original ATen: [aten.add, aten.mul, aten.div]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_div_mul_0.run(buf9, buf17, buf1, buf2, buf3, buf4, buf5, buf6, buf7, buf8, buf11, buf12, buf13, buf14, buf15, buf16, arg0_1, 16, grid=grid(16), stream=stream0)
        del arg0_1
        del buf1
        del buf11
        del buf12
        del buf13
        del buf14
        del buf15
        del buf16
        del buf2
        del buf3
        del buf4
        del buf5
        del buf6
        del buf7
        del buf8
        del buf9
    return (buf17, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

from torch.nn import Module
import torch


class SSGConv(Module):

    def __init__(self, K=16, alpha=0.1, **kwargs):
        super().__init__()
        assert K > 0
        self.K = K
        self.alpha = alpha

    def forward(self, x, adj):
        x_in = x
        x_out = torch.zeros_like(x)
        for _ in range(self.K):
            x = torch.spmm(adj, x)
            x_out += (1 - self.alpha) * x
        x_out /= self.K
        x_out += self.alpha * x_in
        return x_out

    def reset_parameters(self):
        pass

    def extra_repr(self):
        return f'K={self.K}, alpha={self.alpha}'


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


def get_init_inputs():
    return [[], {}]

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 import Module
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_add_div_mul_0(in_out_ptr0, in_out_ptr1, in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, in_ptr8,
    in_ptr9, in_ptr10, in_ptr11, in_ptr12, in_ptr13, in_ptr14, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp3 = tl.load(in_ptr0 + x0, xmask)
    tmp6 = tl.load(in_ptr1 + x0, xmask)
    tmp9 = tl.load(in_ptr2 + x0, xmask)
    tmp12 = tl.load(in_ptr3 + x0, xmask)
    tmp15 = tl.load(in_ptr4 + x0, xmask)
    tmp18 = tl.load(in_ptr5 + x0, xmask)
    tmp21 = tl.load(in_ptr6 + x0, xmask)
    tmp24 = tl.load(in_ptr7 + x0, xmask)
    tmp27 = tl.load(in_out_ptr1 + x0, xmask)
    tmp30 = tl.load(in_ptr8 + x0, xmask)
    tmp33 = tl.load(in_ptr9 + x0, xmask)
    tmp36 = tl.load(in_ptr10 + x0, xmask)
    tmp39 = tl.load(in_ptr11 + x0, xmask)
    tmp42 = tl.load(in_ptr12 + x0, xmask)
    tmp45 = tl.load(in_ptr13 + x0, xmask)
    tmp50 = tl.load(in_ptr14 + x0, xmask)
    tmp1 = 0.9
    tmp2 = tmp0 * tmp1
    tmp4 = tmp3 * tmp1
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp1
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp1
    tmp11 = tmp8 + tmp10
    tmp13 = tmp12 * tmp1
    tmp14 = tmp11 + tmp13
    tmp16 = tmp15 * tmp1
    tmp17 = tmp14 + tmp16
    tmp19 = tmp18 * tmp1
    tmp20 = tmp17 + tmp19
    tmp22 = tmp21 * tmp1
    tmp23 = tmp20 + tmp22
    tmp25 = tmp24 * tmp1
    tmp26 = tmp23 + tmp25
    tmp28 = tmp27 * tmp1
    tmp29 = tmp26 + tmp28
    tmp31 = tmp30 * tmp1
    tmp32 = tmp29 + tmp31
    tmp34 = tmp33 * tmp1
    tmp35 = tmp32 + tmp34
    tmp37 = tmp36 * tmp1
    tmp38 = tmp35 + tmp37
    tmp40 = tmp39 * tmp1
    tmp41 = tmp38 + tmp40
    tmp43 = tmp42 * tmp1
    tmp44 = tmp41 + tmp43
    tmp46 = tmp45 * tmp1
    tmp47 = tmp44 + tmp46
    tmp48 = 0.0625
    tmp49 = tmp47 * tmp48
    tmp51 = 0.1
    tmp52 = tmp50 * tmp51
    tmp53 = tmp49 + tmp52
    tl.store(in_out_ptr1 + x0, tmp53, xmask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    assert_size_stride(arg1_1, (4, 4), (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(arg1_1, arg0_1, out=buf0)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(arg1_1, buf0, out=buf1)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(arg1_1, buf1, out=buf2)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(arg1_1, buf2, out=buf3)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(arg1_1, buf3, out=buf4)
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(arg1_1, buf4, out=buf5)
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(arg1_1, buf5, out=buf6)
        buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(arg1_1, buf6, out=buf7)
        buf8 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(arg1_1, buf7, out=buf8)
        buf10 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(arg1_1, buf8, out=buf10)
        buf11 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(arg1_1, buf10, out=buf11)
        buf12 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(arg1_1, buf11, out=buf12)
        buf13 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(arg1_1, buf12, out=buf13)
        buf14 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(arg1_1, buf13, out=buf14)
        buf15 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(arg1_1, buf14, out=buf15)
        buf16 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(arg1_1, buf15, out=buf16)
        del arg1_1
        buf9 = buf0
        del buf0
        buf17 = buf10
        del buf10
        get_raw_stream(0)
        triton_poi_fused_add_div_mul_0[grid(16)](buf9, buf17, buf1, buf2,
            buf3, buf4, buf5, buf6, buf7, buf8, buf11, buf12, buf13, buf14,
            buf15, buf16, arg0_1, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del arg0_1
        del buf1
        del buf11
        del buf12
        del buf13
        del buf14
        del buf15
        del buf16
        del buf2
        del buf3
        del buf4
        del buf5
        del buf6
        del buf7
        del buf8
        del buf9
    return buf17,


class SSGConvNew(Module):

    def __init__(self, K=16, alpha=0.1, **kwargs):
        super().__init__()
        assert K > 0
        self.K = K
        self.alpha = alpha

    def reset_parameters(self):
        pass

    def extra_repr(self):
        return f'K={self.K}, alpha={self.alpha}'

    def forward(self, input_0, input_1):
        arg0_1 = input_0
        arg1_1 = input_1
        output = call([arg0_1, arg1_1])
        return output[0]
