# 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/ja/cjavyu5r4xu5nlitoyabvy7ofhz4ogm5mu3dk4l67xn2v3fgexma.py
# Topologically Sorted Source Nodes: [D], Original ATen: [aten.diag_embed]
# Source node to ATen node mapping:
#   D => eq, full_default, iota, where
# Graph fragment:
#   %iota : [num_users=3] = call_function[target=torch.ops.prims.iota.default](args = (4,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
#   %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Tensor](args = (%iota, %unsqueeze_1), kwargs = {})
#   %full_default : [num_users=2] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%eq, %permute, %full_default), kwargs = {})
triton_poi_fused_diag_embed_0 = async_compile.triton('triton_poi_fused_diag_embed_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_diag_embed_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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_diag_embed_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
    x0 = xindex % 4
    x1 = (xindex // 4)
    x2 = xindex
    tmp3 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp0 = x0
    tmp1 = x1
    tmp2 = tmp0 == tmp1
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp9 = tmp7 + tmp8
    tmp10 = -0.5
    tmp11 = libdevice.pow(tmp9, tmp10)
    tmp12 = 0.0
    tmp13 = tl.where(tmp2, tmp11, tmp12)
    tl.store(out_ptr0 + (x2), tmp13, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/7m/c7melmuezdwy5c6u3qg5b2dvtctgso6etynkxd74lrziaclsxuvt.py
# Topologically Sorted Source Nodes: [D, eye, L], Original ATen: [aten.diag_embed, aten.eye, aten.sub]
# Source node to ATen node mapping:
#   D => full_default, iota
#   L => sub
#   eye => eq_1, full_default_1, where_1
# Graph fragment:
#   %iota : [num_users=3] = call_function[target=torch.ops.prims.iota.default](args = (4,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
#   %full_default : [num_users=2] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %eq_1 : [num_users=1] = call_function[target=torch.ops.aten.eq.Tensor](args = (%unsqueeze_1, %iota), kwargs = {})
#   %full_default_1 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([1], 1), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where_1 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%eq_1, %full_default_1, %full_default), kwargs = {})
#   %sub : [num_users=4] = call_function[target=torch.ops.aten.sub.Tensor](args = (%where_1, %mm_1), kwargs = {})
triton_poi_fused_diag_embed_eye_sub_1 = async_compile.triton('triton_poi_fused_diag_embed_eye_sub_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: '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_diag_embed_eye_sub_1', 'mutated_arg_names': ['in_out_ptr0'], '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_diag_embed_eye_sub_1(in_out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4)
    x0 = xindex % 4
    x2 = xindex
    tmp6 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp0 = x1
    tmp1 = x0
    tmp2 = tmp0 == tmp1
    tmp3 = 1.0
    tmp4 = 0.0
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp7 = tmp5 - tmp6
    tl.store(in_out_ptr0 + (x2), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/nt/cnt63h74o735agddjfb5clildoy6gfngo35dt46zyissubqboxxq.py
# Topologically Sorted Source Nodes: [D, eye, multi_order_laplacian], Original ATen: [aten.diag_embed, aten.eye, aten.zeros]
# Source node to ATen node mapping:
#   D => full_default, iota
#   eye => eq_1, full_default_1, where_1
#   multi_order_laplacian => full_default_3
# Graph fragment:
#   %iota : [num_users=3] = call_function[target=torch.ops.prims.iota.default](args = (4,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
#   %full_default : [num_users=2] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %eq_1 : [num_users=1] = call_function[target=torch.ops.aten.eq.Tensor](args = (%unsqueeze_1, %iota), kwargs = {})
#   %full_default_1 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([1], 1), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where_1 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%eq_1, %full_default_1, %full_default), kwargs = {})
#   %full_default_3 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([5, 4, 4], 0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %select_scatter_default : [num_users=1] = call_function[target=torch.ops.aten.select_scatter.default](args = (%full_default_3, %where_1, 0, 0), kwargs = {})
#   %select_scatter_default_1 : [num_users=3] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_scatter_default, %sub, 0, 1), kwargs = {})
triton_poi_fused_diag_embed_eye_zeros_2 = async_compile.triton('triton_poi_fused_diag_embed_eye_zeros_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=[128], 
    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_diag_embed_eye_zeros_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_diag_embed_eye_zeros_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 80
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = (xindex // 16)
    x3 = xindex % 16
    x1 = (xindex // 4) % 4
    x0 = xindex % 4
    x4 = xindex
    tmp3 = tl.load(in_ptr0 + (x3), xmask, eviction_policy='evict_last')
    tmp0 = x2
    tmp1 = tl.full([1], 1, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp4 = tl.full([1], 0, tl.int32)
    tmp5 = tmp0 == tmp4
    tmp6 = x1
    tmp7 = x0
    tmp8 = tmp6 == tmp7
    tmp9 = 1.0
    tmp10 = 0.0
    tmp11 = tl.where(tmp8, tmp9, tmp10)
    tmp12 = tl.where(tmp5, tmp11, tmp10)
    tmp13 = tl.where(tmp2, tmp3, tmp12)
    tl.store(out_ptr0 + (x4), tmp13, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/rs/crsoboivz65dylv2rwpdelqiarxf2u4i3uaaebwqwvnjhxkjbjjw.py
# Topologically Sorted Source Nodes: [mul, sub_1], Original ATen: [aten.mul, aten.sub]
# Source node to ATen node mapping:
#   mul => mul
#   sub_1 => sub_1
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm_2, 2), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul, %select_8), kwargs = {})
#   %select_scatter_default_2 : [num_users=3] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_scatter_default_1, %sub_1, 0, 2), kwargs = {})
triton_poi_fused_mul_sub_3 = async_compile.triton('triton_poi_fused_mul_sub_3', '''
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=[128], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_sub_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_mul_sub_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 80
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 16)
    x0 = xindex % 16
    x2 = xindex
    tmp3 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (x2), xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 2, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp4 = 2.0
    tmp5 = tmp3 * tmp4
    tmp7 = tmp5 - tmp6
    tmp9 = tl.where(tmp2, tmp7, tmp8)
    tl.store(out_ptr0 + (x2), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/7w/c7wq3e2ygdgfl4kidxckp4el2aafp77m6tdwbcqmea2hupgvkoek.py
# Topologically Sorted Source Nodes: [mul_1, sub_2], Original ATen: [aten.mul, aten.sub]
# Source node to ATen node mapping:
#   mul_1 => mul_1
#   sub_2 => sub_2
# Graph fragment:
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm_3, 2), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_1, %select_15), kwargs = {})
#   %select_scatter_default_3 : [num_users=3] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_scatter_default_2, %sub_2, 0, 3), kwargs = {})
triton_poi_fused_mul_sub_4 = async_compile.triton('triton_poi_fused_mul_sub_4', '''
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=[128], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_sub_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_mul_sub_4(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 80
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 16)
    x0 = xindex % 16
    x2 = xindex
    tmp3 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (16 + x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (x2), xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 3, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp4 = 2.0
    tmp5 = tmp3 * tmp4
    tmp7 = tmp5 - tmp6
    tmp9 = tl.where(tmp2, tmp7, tmp8)
    tl.store(out_ptr0 + (x2), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/vx/cvxptdlc4xjc2zntndyxuyooh2cl4jzpbya2v26rjm7vmkjhjszi.py
# Topologically Sorted Source Nodes: [mul_2, sub_3], Original ATen: [aten.mul, aten.sub]
# Source node to ATen node mapping:
#   mul_2 => mul_2
#   sub_3 => sub_3
# Graph fragment:
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm_4, 2), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_2, %select_22), kwargs = {})
#   %select_scatter_default_4 : [num_users=1] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_scatter_default_3, %sub_3, 0, 4), kwargs = {})
triton_poi_fused_mul_sub_5 = async_compile.triton('triton_poi_fused_mul_sub_5', '''
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=[128], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_sub_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_mul_sub_5(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 80
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 16)
    x0 = xindex % 16
    x2 = xindex
    tmp3 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (32 + x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (x2), xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 4, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp4 = 2.0
    tmp5 = tmp3 * tmp4
    tmp7 = tmp5 - tmp6
    tmp9 = tl.where(tmp2, tmp7, tmp8)
    tl.store(out_ptr0 + (x2), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/wi/cwija4xxrmds4pzlo5szwl2su3gon4ktmt34eluogiaiavrknbqr.py
# Topologically Sorted Source Nodes: [sum_2, result_2], Original ATen: [aten.sum, aten.add]
# Source node to ATen node mapping:
#   result_2 => add
#   sum_2 => sum_2
# Graph fragment:
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%view_6, [0]), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_2, %primals_4), kwargs = {})
triton_poi_fused_add_sum_6 = async_compile.triton('triton_poi_fused_add_sum_6', '''
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: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_sum_6', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, '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_sum_6(in_ptr0, in_ptr1, 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
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (16 + x2), xmask)
    tmp3 = tl.load(in_ptr0 + (32 + x2), xmask)
    tmp5 = tl.load(in_ptr0 + (48 + x2), xmask)
    tmp7 = tl.load(in_ptr0 + (64 + x2), xmask)
    tmp9 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp8 = tmp6 + tmp7
    tmp10 = tmp8 + tmp9
    tl.store(out_ptr0 + (x2), tmp10, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4 = 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, (5, 1, 4, 4), (16, 16, 4, 1))
    assert_size_stride(primals_4, (1, 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: [D], Original ATen: [aten.diag_embed]
        stream0 = get_raw_stream(0)
        triton_poi_fused_diag_embed_0.run(primals_1, buf0, 16, grid=grid(16), stream=stream0)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mm], Original ATen: [aten.mm]
        extern_kernels.mm(buf0, primals_1, out=buf1)
        del primals_1
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mm_1], Original ATen: [aten.mm]
        extern_kernels.mm(buf1, buf0, out=buf2)
        del buf0
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [D, eye, L], Original ATen: [aten.diag_embed, aten.eye, aten.sub]
        triton_poi_fused_diag_embed_eye_sub_1.run(buf3, 16, grid=grid(16), stream=stream0)
        buf4 = empty_strided_cuda((5, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [D, eye, multi_order_laplacian], Original ATen: [aten.diag_embed, aten.eye, aten.zeros]
        triton_poi_fused_diag_embed_eye_zeros_2.run(buf3, buf4, 80, grid=grid(80), stream=stream0)
        buf5 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [mm_2], Original ATen: [aten.mm]
        extern_kernels.mm(buf3, reinterpret_tensor(buf4, (4, 4), (4, 1), 16), out=buf5)
        buf6 = empty_strided_cuda((5, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul, sub_1], Original ATen: [aten.mul, aten.sub]
        triton_poi_fused_mul_sub_3.run(buf5, buf4, buf6, 80, grid=grid(80), stream=stream0)
        buf7 = buf5; del buf5  # reuse
        # Topologically Sorted Source Nodes: [mm_3], Original ATen: [aten.mm]
        extern_kernels.mm(buf3, reinterpret_tensor(buf6, (4, 4), (4, 1), 32), out=buf7)
        buf8 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [mul_1, sub_2], Original ATen: [aten.mul, aten.sub]
        triton_poi_fused_mul_sub_4.run(buf7, buf6, buf8, 80, grid=grid(80), stream=stream0)
        buf9 = buf7; del buf7  # reuse
        # Topologically Sorted Source Nodes: [mm_4], Original ATen: [aten.mm]
        extern_kernels.mm(buf3, reinterpret_tensor(buf8, (4, 4), (4, 1), 48), out=buf9)
        del buf3
        buf10 = buf6; del buf6  # reuse
        # Topologically Sorted Source Nodes: [mul_2, sub_3], Original ATen: [aten.mul, aten.sub]
        triton_poi_fused_mul_sub_5.run(buf9, buf8, buf10, 80, grid=grid(80), stream=stream0)
        buf11 = reinterpret_tensor(buf8, (20, 4), (4, 1), 0); del buf8  # reuse
        # Topologically Sorted Source Nodes: [result], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf10, (20, 4), (4, 1), 0), primals_2, out=buf11)
        del primals_2
        buf12 = buf10; del buf10  # reuse
        # Topologically Sorted Source Nodes: [result_1], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf11, (5, 4, 4), (16, 4, 1), 0), reinterpret_tensor(primals_3, (5, 4, 4), (16, 4, 1), 0), out=buf12)
        del primals_3
        buf13 = reinterpret_tensor(buf9, (1, 4, 4), (16, 4, 1), 0); del buf9  # reuse
        # Topologically Sorted Source Nodes: [sum_2, result_2], Original ATen: [aten.sum, aten.add]
        triton_poi_fused_add_sum_6.run(buf12, primals_4, buf13, 16, grid=grid(16), stream=stream0)
        del buf12
        del primals_4
    return (buf13, reinterpret_tensor(buf11, (5, 4, 4), (16, 1, 4), 0), )


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((5, 1, 4, 4), (16, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((1, 1, 4), (4, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
    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
import torch.nn.init as init


class ChebConv(nn.Module):
    """
    The ChebNet convolution operation.

    :param in_c: int, number of input dim
    :param out_c: int, number of output dim
    :param K: int, the order of Chebyshev Polynomial,切比雪夫展开多少阶
    """

    def __init__(self, in_c, out_c, K, bias=True, normalize=True):
        super(ChebConv, self).__init__()
        self.normalize = normalize
        self.weight = nn.Parameter(torch.Tensor(K + 1, 1, in_c, out_c))
        init.xavier_normal_(self.weight)
        if bias:
            self.bias = nn.Parameter(torch.Tensor(1, 1, out_c))
            init.zeros_(self.bias)
        else:
            self.register_parameter('bias', None)
        self.K = K + 1

    def forward(self, inputs, graph):
        """
        :param inputs: the input data, [B, N, C]
        :param graph: the graph structure, [N, N]
        :return: convolution result, [B, N, D]
        """
        L = ChebConv.get_laplacian(graph, self.normalize)
        mul_L = self.cheb_polynomial(L).unsqueeze(1)
        result = torch.matmul(mul_L, inputs)
        result = torch.matmul(result, self.weight)
        result = torch.sum(result, dim=0) + self.bias
        return result

    def cheb_polynomial(self, laplacian):
        """
        Compute the Chebyshev Polynomial, according to the graph laplacian.

        :param laplacian: the graph laplacian, [N, N].
        :return: the multi order Chebyshev laplacian, [K, N, N].
        """
        N = laplacian.size(0)
        multi_order_laplacian = torch.zeros([self.K, N, N], device=
            laplacian.device, dtype=torch.float)
        multi_order_laplacian[0] = torch.eye(N, device=laplacian.device,
            dtype=torch.float)
        if self.K == 1:
            return multi_order_laplacian
        else:
            multi_order_laplacian[1] = laplacian
            if self.K == 2:
                return multi_order_laplacian
            else:
                for k in range(2, self.K):
                    multi_order_laplacian[k] = 2 * torch.mm(laplacian,
                        multi_order_laplacian[k - 1]) - multi_order_laplacian[
                        k - 2]
        return multi_order_laplacian

    @staticmethod
    def get_laplacian(graph, normalize):
        """
        return the laplacian of the graph.

        :param graph: the graph structure without self loop, [N, N].
        :param normalize: whether to used the normalized laplacian.
        :return: graph laplacian.
        """
        if normalize:
            D = torch.diag(torch.sum(graph, dim=-1) ** (-1 / 2))
            L = torch.eye(graph.size(0), device=graph.device, dtype=graph.dtype
                ) - torch.mm(torch.mm(D, graph), D)
        else:
            D = torch.diag(torch.sum(graph, dim=-1))
            L = D - graph
        return L


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


def get_init_inputs():
    return [[], {'in_c': 4, 'out_c': 4, 'K': 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._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.nn.init as init
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_diag_embed_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
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp3 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp0 = x0
    tmp1 = x1
    tmp2 = tmp0 == tmp1
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp9 = tmp7 + tmp8
    tmp10 = -0.5
    tmp11 = libdevice.pow(tmp9, tmp10)
    tmp12 = 0.0
    tmp13 = tl.where(tmp2, tmp11, tmp12)
    tl.store(out_ptr0 + x2, tmp13, xmask)


@triton.jit
def triton_poi_fused_diag_embed_eye_sub_1(in_out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x0 = xindex % 4
    x2 = xindex
    tmp6 = tl.load(in_out_ptr0 + x2, xmask)
    tmp0 = x1
    tmp1 = x0
    tmp2 = tmp0 == tmp1
    tmp3 = 1.0
    tmp4 = 0.0
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp7 = tmp5 - tmp6
    tl.store(in_out_ptr0 + x2, tmp7, xmask)


@triton.jit
def triton_poi_fused_diag_embed_eye_zeros_2(in_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 80
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 16
    x3 = xindex % 16
    x1 = xindex // 4 % 4
    x0 = xindex % 4
    x4 = xindex
    tmp3 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last')
    tmp0 = x2
    tmp1 = tl.full([1], 1, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp4 = tl.full([1], 0, tl.int32)
    tmp5 = tmp0 == tmp4
    tmp6 = x1
    tmp7 = x0
    tmp8 = tmp6 == tmp7
    tmp9 = 1.0
    tmp10 = 0.0
    tmp11 = tl.where(tmp8, tmp9, tmp10)
    tmp12 = tl.where(tmp5, tmp11, tmp10)
    tmp13 = tl.where(tmp2, tmp3, tmp12)
    tl.store(out_ptr0 + x4, tmp13, xmask)


@triton.jit
def triton_poi_fused_mul_sub_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 80
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16
    x0 = xindex % 16
    x2 = xindex
    tmp3 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + x2, xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 2, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp4 = 2.0
    tmp5 = tmp3 * tmp4
    tmp7 = tmp5 - tmp6
    tmp9 = tl.where(tmp2, tmp7, tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused_mul_sub_4(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 80
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16
    x0 = xindex % 16
    x2 = xindex
    tmp3 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (16 + x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + x2, xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 3, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp4 = 2.0
    tmp5 = tmp3 * tmp4
    tmp7 = tmp5 - tmp6
    tmp9 = tl.where(tmp2, tmp7, tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused_mul_sub_5(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 80
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16
    x0 = xindex % 16
    x2 = xindex
    tmp3 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (32 + x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + x2, xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 4, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp4 = 2.0
    tmp5 = tmp3 * tmp4
    tmp7 = tmp5 - tmp6
    tmp9 = tl.where(tmp2, tmp7, tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused_add_sum_6(in_ptr0, in_ptr1, 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
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + (16 + x2), xmask)
    tmp3 = tl.load(in_ptr0 + (32 + x2), xmask)
    tmp5 = tl.load(in_ptr0 + (48 + x2), xmask)
    tmp7 = tl.load(in_ptr0 + (64 + x2), xmask)
    tmp9 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp8 = tmp6 + tmp7
    tmp10 = tmp8 + tmp9
    tl.store(out_ptr0 + x2, tmp10, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = 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, (5, 1, 4, 4), (16, 16, 4, 1))
    assert_size_stride(primals_4, (1, 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)
        get_raw_stream(0)
        triton_poi_fused_diag_embed_0[grid(16)](primals_1, buf0, 16, XBLOCK
            =16, num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf0, primals_1, out=buf1)
        del primals_1
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf1, buf0, out=buf2)
        del buf0
        buf3 = buf2
        del buf2
        triton_poi_fused_diag_embed_eye_sub_1[grid(16)](buf3, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf4 = empty_strided_cuda((5, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_diag_embed_eye_zeros_2[grid(80)](buf3, buf4, 80,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf5 = buf1
        del buf1
        extern_kernels.mm(buf3, reinterpret_tensor(buf4, (4, 4), (4, 1), 16
            ), out=buf5)
        buf6 = empty_strided_cuda((5, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_mul_sub_3[grid(80)](buf5, buf4, buf6, 80, XBLOCK=
            128, num_warps=4, num_stages=1)
        buf7 = buf5
        del buf5
        extern_kernels.mm(buf3, reinterpret_tensor(buf6, (4, 4), (4, 1), 32
            ), out=buf7)
        buf8 = buf4
        del buf4
        triton_poi_fused_mul_sub_4[grid(80)](buf7, buf6, buf8, 80, XBLOCK=
            128, num_warps=4, num_stages=1)
        buf9 = buf7
        del buf7
        extern_kernels.mm(buf3, reinterpret_tensor(buf8, (4, 4), (4, 1), 48
            ), out=buf9)
        del buf3
        buf10 = buf6
        del buf6
        triton_poi_fused_mul_sub_5[grid(80)](buf9, buf8, buf10, 80, XBLOCK=
            128, num_warps=4, num_stages=1)
        buf11 = reinterpret_tensor(buf8, (20, 4), (4, 1), 0)
        del buf8
        extern_kernels.mm(reinterpret_tensor(buf10, (20, 4), (4, 1), 0),
            primals_2, out=buf11)
        del primals_2
        buf12 = buf10
        del buf10
        extern_kernels.bmm(reinterpret_tensor(buf11, (5, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(primals_3, (5, 4, 4), (16, 4, 1), 0),
            out=buf12)
        del primals_3
        buf13 = reinterpret_tensor(buf9, (1, 4, 4), (16, 4, 1), 0)
        del buf9
        triton_poi_fused_add_sum_6[grid(16)](buf12, primals_4, buf13, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del buf12
        del primals_4
    return buf13, reinterpret_tensor(buf11, (5, 4, 4), (16, 1, 4), 0)


class ChebConvNew(nn.Module):
    """
    The ChebNet convolution operation.

    :param in_c: int, number of input dim
    :param out_c: int, number of output dim
    :param K: int, the order of Chebyshev Polynomial,切比雪夫展开多少阶
    """

    def __init__(self, in_c, out_c, K, bias=True, normalize=True):
        super(ChebConvNew, self).__init__()
        self.normalize = normalize
        self.weight = nn.Parameter(torch.Tensor(K + 1, 1, in_c, out_c))
        init.xavier_normal_(self.weight)
        if bias:
            self.bias = nn.Parameter(torch.Tensor(1, 1, out_c))
            init.zeros_(self.bias)
        else:
            self.register_parameter('bias', None)
        self.K = K + 1

    def cheb_polynomial(self, laplacian):
        """
        Compute the Chebyshev Polynomial, according to the graph laplacian.

        :param laplacian: the graph laplacian, [N, N].
        :return: the multi order Chebyshev laplacian, [K, N, N].
        """
        N = laplacian.size(0)
        multi_order_laplacian = torch.zeros([self.K, N, N], device=
            laplacian.device, dtype=torch.float)
        multi_order_laplacian[0] = torch.eye(N, device=laplacian.device,
            dtype=torch.float)
        if self.K == 1:
            return multi_order_laplacian
        else:
            multi_order_laplacian[1] = laplacian
            if self.K == 2:
                return multi_order_laplacian
            else:
                for k in range(2, self.K):
                    multi_order_laplacian[k] = 2 * torch.mm(laplacian,
                        multi_order_laplacian[k - 1]) - multi_order_laplacian[
                        k - 2]
        return multi_order_laplacian

    @staticmethod
    def get_laplacian(graph, normalize):
        """
        return the laplacian of the graph.

        :param graph: the graph structure without self loop, [N, N].
        :param normalize: whether to used the normalized laplacian.
        :return: graph laplacian.
        """
        if normalize:
            D = torch.diag(torch.sum(graph, dim=-1) ** (-1 / 2))
            L = torch.eye(graph.size(0), device=graph.device, dtype=graph.dtype
                ) - torch.mm(torch.mm(D, graph), D)
        else:
            D = torch.diag(torch.sum(graph, dim=-1))
            L = D - graph
        return L

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