# AOT ID: ['0_forward']
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/jj/cjjiphzf24trw5axa2pmjeyaxi43ixlzlidljcludzymowb4xjf4.py
# Topologically Sorted Source Nodes: [h], Original ATen: [aten.stack]
# Source node to ATen node mapping:
#   h => cat
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_2, %mm, %mm_1, %mm_2, %mm_3, %mm_4, %mm_5, %mm_6, %mm_7, %mm_8, %mm_9], 1), kwargs = {})
triton_poi_fused_stack_0 = async_compile.triton('triton_poi_fused_stack_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: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_stack_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_stack_0(in_ptr0, out_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
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tl.store(out_ptr0 + (x0 + (44*x1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/vz/cvz2b43gw7evjx7y4go5psaewdyw7vxpnuzbnsgioqvsaqvketru.py
# Topologically Sorted Source Nodes: [h], Original ATen: [aten.stack]
# Source node to ATen node mapping:
#   h => cat
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_2, %mm, %mm_1, %mm_2, %mm_3, %mm_4, %mm_5, %mm_6, %mm_7, %mm_8, %mm_9], 1), kwargs = {})
triton_poi_fused_stack_1 = async_compile.triton('triton_poi_fused_stack_1', '''
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: '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, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_stack_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_stack_1(in_ptr0, out_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
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tl.store(out_ptr0 + (x0 + (44*x1)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/2v/c2v75im7azf6ydjugomrvbgk2qfdv2227zl2y3hb44a525i6c3ql.py
# Topologically Sorted Source Nodes: [sigmoid], Original ATen: [aten.sigmoid]
# Source node to ATen node mapping:
#   sigmoid => sigmoid
# Graph fragment:
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%view_2,), kwargs = {})
triton_poi_fused_sigmoid_2 = async_compile.triton('triton_poi_fused_sigmoid_2', '''
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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_sigmoid_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_sigmoid_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 44
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = tl.sigmoid(tmp0)
    tl.store(out_ptr0 + (x0), tmp1, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = 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, (1, 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(primals_1, primals_2, 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(primals_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(primals_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(primals_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(primals_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(primals_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(primals_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(primals_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(primals_1, buf7, out=buf8)
        buf20 = empty_strided_cuda((4, 44), (44, 1), torch.float32)
        buf9 = reinterpret_tensor(buf20, (4, 4), (44, 1), 40)  # alias
        # Topologically Sorted Source Nodes: [x_9], Original ATen: [aten.mm]
        extern_kernels.mm(primals_1, buf8, out=buf9)
        del primals_1
        buf10 = reinterpret_tensor(buf20, (4, 4), (44, 1), 0)  # alias
        # Topologically Sorted Source Nodes: [h], Original ATen: [aten.stack]
        stream0 = get_raw_stream(0)
        triton_poi_fused_stack_0.run(primals_2, buf10, 16, grid=grid(16), stream=stream0)
        del primals_2
        buf11 = reinterpret_tensor(buf20, (4, 4), (44, 1), 4)  # alias
        # Topologically Sorted Source Nodes: [h], Original ATen: [aten.stack]
        triton_poi_fused_stack_1.run(buf0, buf11, 16, grid=grid(16), stream=stream0)
        del buf0
        buf12 = reinterpret_tensor(buf20, (4, 4), (44, 1), 8)  # alias
        # Topologically Sorted Source Nodes: [h], Original ATen: [aten.stack]
        triton_poi_fused_stack_1.run(buf1, buf12, 16, grid=grid(16), stream=stream0)
        del buf1
        buf13 = reinterpret_tensor(buf20, (4, 4), (44, 1), 12)  # alias
        # Topologically Sorted Source Nodes: [h], Original ATen: [aten.stack]
        triton_poi_fused_stack_1.run(buf2, buf13, 16, grid=grid(16), stream=stream0)
        del buf2
        buf14 = reinterpret_tensor(buf20, (4, 4), (44, 1), 16)  # alias
        # Topologically Sorted Source Nodes: [h], Original ATen: [aten.stack]
        triton_poi_fused_stack_0.run(buf3, buf14, 16, grid=grid(16), stream=stream0)
        del buf3
        buf15 = reinterpret_tensor(buf20, (4, 4), (44, 1), 20)  # alias
        # Topologically Sorted Source Nodes: [h], Original ATen: [aten.stack]
        triton_poi_fused_stack_1.run(buf4, buf15, 16, grid=grid(16), stream=stream0)
        del buf4
        buf16 = reinterpret_tensor(buf20, (4, 4), (44, 1), 24)  # alias
        # Topologically Sorted Source Nodes: [h], Original ATen: [aten.stack]
        triton_poi_fused_stack_1.run(buf5, buf16, 16, grid=grid(16), stream=stream0)
        del buf5
        buf17 = reinterpret_tensor(buf20, (4, 4), (44, 1), 28)  # alias
        # Topologically Sorted Source Nodes: [h], Original ATen: [aten.stack]
        triton_poi_fused_stack_1.run(buf6, buf17, 16, grid=grid(16), stream=stream0)
        del buf6
        buf18 = reinterpret_tensor(buf20, (4, 4), (44, 1), 32)  # alias
        # Topologically Sorted Source Nodes: [h], Original ATen: [aten.stack]
        triton_poi_fused_stack_0.run(buf7, buf18, 16, grid=grid(16), stream=stream0)
        del buf7
        buf19 = reinterpret_tensor(buf20, (4, 4), (44, 1), 36)  # alias
        # Topologically Sorted Source Nodes: [h], Original ATen: [aten.stack]
        triton_poi_fused_stack_1.run(buf8, buf19, 16, grid=grid(16), stream=stream0)
        buf21 = empty_strided_cuda((44, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf20, (44, 4), (4, 1), 0), reinterpret_tensor(primals_3, (4, 1), (1, 4), 0), out=buf21)
        del primals_3
        buf22 = empty_strided_cuda((4, 11, 1), (11, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sigmoid], Original ATen: [aten.sigmoid]
        triton_poi_fused_sigmoid_2.run(buf21, buf22, 44, grid=grid(44), stream=stream0)
        buf23 = reinterpret_tensor(buf8, (4, 1, 4), (4, 4, 1), 0); del buf8  # reuse
        # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf22, (4, 1, 11), (11, 0, 1), 0), reinterpret_tensor(buf20, (4, 11, 4), (44, 4, 1), 0), out=buf23)
        del buf22
    return (reinterpret_tensor(buf23, (4, 4), (4, 1), 0), buf20, buf21, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3])
    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)

import torch
import torch.nn as nn


class DAGNNConv(nn.Module):

    def __init__(self, in_features, out_features=1, K=10, bias=False):
        super().__init__()
        assert out_features == 1, "'out_features' must be 1"
        self.in_features = in_features
        self.out_features = out_features
        self.lin = nn.Linear(in_features, out_features, bias=bias)
        self.K = K
        self.act = nn.Sigmoid()

    def reset_parameters(self):
        self.lin.reset_parameters()

    def forward(self, x, adj):
        propagations = [x]
        for _ in range(self.K):
            x = adj.mm(x)
            propagations.append(x)
        h = torch.stack(propagations, dim=1)
        retain_score = self.act(self.lin(h)).permute(0, 2, 1).contiguous()
        out = (retain_score @ h).squeeze(1)
        return out

    def __repr__(self):
        return (
            f'{self.__class__.__name__}({self.in_features}, {self.out_features}, K={self.K})'
            )


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


def get_init_inputs():
    return [[], {'in_features': 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
import torch.nn as nn
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_stack_0(in_ptr0, out_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
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tl.store(out_ptr0 + (x0 + 44 * x1), tmp0, xmask)


@triton.jit
def triton_poi_fused_stack_1(in_ptr0, out_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
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tl.store(out_ptr0 + (x0 + 44 * x1), tmp0, xmask)


@triton.jit
def triton_poi_fused_sigmoid_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 44
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.sigmoid(tmp0)
    tl.store(out_ptr0 + x0, tmp1, xmask)


def call(args):
    primals_1, primals_2, primals_3 = 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, (1, 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(primals_1, primals_2, out=buf0)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_1, buf0, out=buf1)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_1, buf1, out=buf2)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_1, buf2, out=buf3)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_1, buf3, out=buf4)
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_1, buf4, out=buf5)
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_1, buf5, out=buf6)
        buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_1, buf6, out=buf7)
        buf8 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_1, buf7, out=buf8)
        buf20 = empty_strided_cuda((4, 44), (44, 1), torch.float32)
        buf9 = reinterpret_tensor(buf20, (4, 4), (44, 1), 40)
        extern_kernels.mm(primals_1, buf8, out=buf9)
        del primals_1
        buf10 = reinterpret_tensor(buf20, (4, 4), (44, 1), 0)
        get_raw_stream(0)
        triton_poi_fused_stack_0[grid(16)](primals_2, buf10, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_2
        buf11 = reinterpret_tensor(buf20, (4, 4), (44, 1), 4)
        triton_poi_fused_stack_1[grid(16)](buf0, buf11, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del buf0
        buf12 = reinterpret_tensor(buf20, (4, 4), (44, 1), 8)
        triton_poi_fused_stack_1[grid(16)](buf1, buf12, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del buf1
        buf13 = reinterpret_tensor(buf20, (4, 4), (44, 1), 12)
        triton_poi_fused_stack_1[grid(16)](buf2, buf13, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del buf2
        buf14 = reinterpret_tensor(buf20, (4, 4), (44, 1), 16)
        triton_poi_fused_stack_0[grid(16)](buf3, buf14, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del buf3
        buf15 = reinterpret_tensor(buf20, (4, 4), (44, 1), 20)
        triton_poi_fused_stack_1[grid(16)](buf4, buf15, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del buf4
        buf16 = reinterpret_tensor(buf20, (4, 4), (44, 1), 24)
        triton_poi_fused_stack_1[grid(16)](buf5, buf16, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del buf5
        buf17 = reinterpret_tensor(buf20, (4, 4), (44, 1), 28)
        triton_poi_fused_stack_1[grid(16)](buf6, buf17, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del buf6
        buf18 = reinterpret_tensor(buf20, (4, 4), (44, 1), 32)
        triton_poi_fused_stack_0[grid(16)](buf7, buf18, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del buf7
        buf19 = reinterpret_tensor(buf20, (4, 4), (44, 1), 36)
        triton_poi_fused_stack_1[grid(16)](buf8, buf19, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf21 = empty_strided_cuda((44, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf20, (44, 4), (4, 1), 0),
            reinterpret_tensor(primals_3, (4, 1), (1, 4), 0), out=buf21)
        del primals_3
        buf22 = empty_strided_cuda((4, 11, 1), (11, 1, 1), torch.float32)
        triton_poi_fused_sigmoid_2[grid(44)](buf21, buf22, 44, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf23 = reinterpret_tensor(buf8, (4, 1, 4), (4, 4, 1), 0)
        del buf8
        extern_kernels.bmm(reinterpret_tensor(buf22, (4, 1, 11), (11, 0, 1),
            0), reinterpret_tensor(buf20, (4, 11, 4), (44, 4, 1), 0), out=buf23
            )
        del buf22
    return reinterpret_tensor(buf23, (4, 4), (4, 1), 0), buf20, buf21


class DAGNNConvNew(nn.Module):

    def __init__(self, in_features, out_features=1, K=10, bias=False):
        super().__init__()
        assert out_features == 1, "'out_features' must be 1"
        self.in_features = in_features
        self.out_features = out_features
        self.lin = nn.Linear(in_features, out_features, bias=bias)
        self.K = K
        self.act = nn.Sigmoid()

    def reset_parameters(self):
        self.lin.reset_parameters()

    def __repr__(self):
        return (
            f'{self.__class__.__name__}({self.in_features}, {self.out_features}, K={self.K})'
            )

    def forward(self, input_0, input_1):
        primals_3 = self.lin.weight
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3])
        return output[0]
