# 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/tm/ctmozaf6zzzqx7pt6secsooq4mfch6oaw25l6twsjt6a4e3m2s43.py
# Topologically Sorted Source Nodes: [diag_embed, abs_1], Original ATen: [aten.diag_embed, aten.abs]
# Source node to ATen node mapping:
#   abs_1 => abs_1
#   diag_embed => eq, full_default, iota, where
# Graph fragment:
#   %iota : [num_users=2] = 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=2] = call_function[target=torch.ops.aten.eq.Tensor](args = (%iota, %unsqueeze_1), kwargs = {})
#   %full_default : [num_users=3] = 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 = {})
#   %abs_1 : [num_users=2] = call_function[target=torch.ops.aten.abs.default](args = (%where,), kwargs = {})
triton_poi_fused_abs_diag_embed_0 = async_compile.triton('triton_poi_fused_abs_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: '*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_abs_diag_embed_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_abs_diag_embed_0(in_ptr0, out_ptr0, out_ptr1, 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 + (x0), xmask, eviction_policy='evict_last')
    tmp0 = x0
    tmp1 = x1
    tmp2 = tmp0 == tmp1
    tmp4 = 0.0
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = tl_math.abs(tmp5)
    tl.store(out_ptr0 + (x2), tmp5, xmask)
    tl.store(out_ptr1 + (x2), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/d7/cd7ww6mspk4u7k74x3mo4ulpi3j442qlg4bmwuqhalvhisxrywgb.py
# Topologically Sorted Source Nodes: [diag_embed, D_inv_1, D_inv_2, setitem, setitem_1], Original ATen: [aten.diag_embed, aten.pow, aten.lift_fresh, aten.index_put]
# Source node to ATen node mapping:
#   D_inv_1 => where_2
#   D_inv_2 => pow_1
#   diag_embed => full_default
#   setitem => full_default_3, index_put
#   setitem_1 => index_put_1
# Graph fragment:
#   %full_default : [num_users=3] = 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_2 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%view_1, %permute_2, %full_default), kwargs = {})
#   %pow_1 : [num_users=2] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%where_2, -0.5), kwargs = {})
#   %full_default_3 : [num_users=4] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cpu, pin_memory: False})
#   %index_put : [num_users=2] = call_function[target=torch.ops.aten.index_put_.default](args = (%pow_1, [%eq_3], %full_default_3), kwargs = {})
#   %index_put_1 : [num_users=3] = call_function[target=torch.ops.aten.index_put_.default](args = (%index_put, [%eq_4], %full_default_3), kwargs = {})
triton_poi_fused_diag_embed_index_put_lift_fresh_pow_1 = async_compile.triton('triton_poi_fused_diag_embed_index_put_lift_fresh_pow_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=[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, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_diag_embed_index_put_lift_fresh_pow_1', 'mutated_arg_names': ['in_out_ptr0'], '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_index_put_lift_fresh_pow_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = (xindex // 4) % 4
    x2 = (xindex // 16)
    x3 = xindex
    tmp3 = tl.load(in_ptr0 + ((4*x0) + (16*x2)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (1 + (4*x0) + (16*x2)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + (4*x0) + (16*x2)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (3 + (4*x0) + (16*x2)), xmask, eviction_policy='evict_last')
    tmp0 = x0
    tmp1 = x1
    tmp2 = tmp0 == tmp1
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp9 = tmp7 + tmp8
    tmp10 = 0.0
    tmp11 = tl.where(tmp2, tmp9, tmp10)
    tmp12 = -0.5
    tmp13 = libdevice.pow(tmp11, tmp12)
    tmp14 = float("inf")
    tmp15 = tmp13 == tmp14
    tmp16 = tl.where(tmp15, tmp10, tmp13)
    tmp17 = float("nan")
    tmp18 = tmp16 == tmp17
    tmp19 = tl.where(tmp18, tmp10, tmp16)
    tl.store(in_out_ptr0 + (x3), tmp19, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ax/caxb3qq4gz4twaq6t5dix7m3syasrfm2snorjxtkh4q3shlnayfe.py
# Topologically Sorted Source Nodes: [diag_embed, B_inv_1, B_inv_2, setitem, setitem_2, setitem_3], Original ATen: [aten.diag_embed, aten.pow, aten.lift_fresh, aten.index_put]
# Source node to ATen node mapping:
#   B_inv_1 => where_1
#   B_inv_2 => pow_2
#   diag_embed => full_default
#   setitem => full_default_3
#   setitem_2 => index_put_2
#   setitem_3 => index_put_3
# Graph fragment:
#   %full_default : [num_users=3] = 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_1 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%view_1, %permute_1, %full_default), kwargs = {})
#   %pow_2 : [num_users=2] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%where_1, -1), kwargs = {})
#   %full_default_3 : [num_users=4] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cpu, pin_memory: False})
#   %index_put_2 : [num_users=2] = call_function[target=torch.ops.aten.index_put_.default](args = (%pow_2, [%eq_5], %full_default_3), kwargs = {})
#   %index_put_3 : [num_users=2] = call_function[target=torch.ops.aten.index_put_.default](args = (%index_put_2, [%eq_6], %full_default_3), kwargs = {})
triton_poi_fused_diag_embed_index_put_lift_fresh_pow_2 = async_compile.triton('triton_poi_fused_diag_embed_index_put_lift_fresh_pow_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, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_diag_embed_index_put_lift_fresh_pow_2', 'mutated_arg_names': ['in_out_ptr0'], '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_index_put_lift_fresh_pow_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = (xindex // 4) % 4
    x2 = (xindex // 16)
    x3 = xindex
    tmp3 = tl.load(in_ptr0 + (x0 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (4 + x0 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (8 + x0 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (12 + x0 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp0 = x0
    tmp1 = x1
    tmp2 = tmp0 == tmp1
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp9 = tmp7 + tmp8
    tmp10 = 0.0
    tmp11 = tl.where(tmp2, tmp9, tmp10)
    tmp12 = tl.full([1], 1, tl.int32)
    tmp13 = tmp12 / tmp11
    tmp14 = float("inf")
    tmp15 = tmp13 == tmp14
    tmp16 = tl.where(tmp15, tmp10, tmp13)
    tmp17 = float("nan")
    tmp18 = tmp16 == tmp17
    tmp19 = tl.where(tmp18, tmp10, tmp16)
    tl.store(in_out_ptr0 + (x3), tmp19, 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, ), (1, ))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [diag_embed, abs_1], Original ATen: [aten.diag_embed, aten.abs]
        stream0 = get_raw_stream(0)
        triton_poi_fused_abs_diag_embed_0.run(primals_1, buf0, buf1, 16, grid=grid(16), stream=stream0)
        del primals_1
        buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_2, (16, 4), (4, 1), 0), buf1, out=buf2)
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf4 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [diag_embed, D_inv_1, D_inv_2, setitem, setitem_1], Original ATen: [aten.diag_embed, aten.pow, aten.lift_fresh, aten.index_put]
        triton_poi_fused_diag_embed_index_put_lift_fresh_pow_1.run(buf4, buf2, 64, grid=grid(64), stream=stream0)
        buf5 = reinterpret_tensor(buf2, (4, 4, 4), (16, 4, 1), 0); del buf2  # reuse
        buf6 = buf5; del buf5  # reuse
        # Topologically Sorted Source Nodes: [diag_embed, B_inv_1, B_inv_2, setitem, setitem_2, setitem_3], Original ATen: [aten.diag_embed, aten.pow, aten.lift_fresh, aten.index_put]
        triton_poi_fused_diag_embed_index_put_lift_fresh_pow_2.run(buf6, primals_2, 64, grid=grid(64), stream=stream0)
        buf7 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [A], Original ATen: [aten.bmm]
        extern_kernels.bmm(buf4, primals_2, out=buf7)
        buf8 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [A_1], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf7, (16, 4), (4, 1), 0), buf1, out=buf8)
        del buf1
        buf9 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [A_2], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf8, (4, 4, 4), (16, 4, 1), 0), buf6, out=buf9)
        buf10 = reinterpret_tensor(buf8, (4, 4, 4), (16, 4, 1), 0); del buf8  # reuse
        # Topologically Sorted Source Nodes: [A_3], Original ATen: [aten.bmm]
        extern_kernels.bmm(buf9, reinterpret_tensor(primals_2, (4, 4, 4), (16, 1, 4), 0), out=buf10)
        buf11 = buf9; del buf9  # reuse
        # Topologically Sorted Source Nodes: [A_4], Original ATen: [aten.bmm]
        extern_kernels.bmm(buf10, buf4, out=buf11)
        buf12 = buf10; del buf10  # reuse
        # Topologically Sorted Source Nodes: [X], Original ATen: [aten.bmm]
        extern_kernels.bmm(buf11, primals_3, out=buf12)
        del buf11
    return (buf12, buf0, buf0, reinterpret_tensor(primals_3, (4, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf4, (4, 4, 4), (16, 1, 4), 0), primals_2, reinterpret_tensor(buf6, (4, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf7, (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, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4), (16, 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 HGCN(nn.Module):

    def __init__(self, n_edges, in_feature, out_feature, n_agents):
        super(HGCN, self).__init__()
        None
        self.W_line = nn.Parameter(torch.ones(n_edges))
        self.W = None

    def forward(self, node_features, hyper_graph):
        self.W = torch.diag_embed(self.W_line)
        B_inv = torch.sum(hyper_graph.detach(), dim=-2)
        B_inv = torch.diag_embed(B_inv)
        softmax_w = torch.abs(self.W).detach()
        D_inv = torch.matmul(hyper_graph.detach(), softmax_w).sum(dim=-1)
        D_inv = torch.diag_embed(D_inv)
        D_inv = D_inv ** -0.5
        B_inv = B_inv ** -1
        D_inv[D_inv == float('inf')] = 0
        D_inv[D_inv == float('nan')] = 0
        B_inv[B_inv == float('inf')] = 0
        B_inv[B_inv == float('nan')] = 0
        A = torch.bmm(D_inv, hyper_graph)
        A = torch.matmul(A, torch.abs(self.W))
        A = torch.bmm(A, B_inv)
        A = torch.bmm(A, hyper_graph.transpose(-2, -1))
        A = torch.bmm(A, D_inv)
        X = torch.bmm(A, node_features)
        return X


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


def get_init_inputs():
    return [[], {'n_edges': 4, 'in_feature': 4, 'out_feature': 4,
        'n_agents': 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, math as tl_math
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_abs_diag_embed_0(in_ptr0, out_ptr0, out_ptr1, 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 + x0, xmask, eviction_policy='evict_last')
    tmp0 = x0
    tmp1 = x1
    tmp2 = tmp0 == tmp1
    tmp4 = 0.0
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = tl_math.abs(tmp5)
    tl.store(out_ptr0 + x2, tmp5, xmask)
    tl.store(out_ptr1 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_diag_embed_index_put_lift_fresh_pow_1(in_out_ptr0,
    in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16
    x3 = xindex
    tmp3 = tl.load(in_ptr0 + (4 * x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (1 + 4 * x0 + 16 * x2), xmask, eviction_policy
        ='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + 4 * x0 + 16 * x2), xmask, eviction_policy
        ='evict_last')
    tmp8 = tl.load(in_ptr0 + (3 + 4 * x0 + 16 * x2), xmask, eviction_policy
        ='evict_last')
    tmp0 = x0
    tmp1 = x1
    tmp2 = tmp0 == tmp1
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp9 = tmp7 + tmp8
    tmp10 = 0.0
    tmp11 = tl.where(tmp2, tmp9, tmp10)
    tmp12 = -0.5
    tmp13 = libdevice.pow(tmp11, tmp12)
    tmp14 = float('inf')
    tmp15 = tmp13 == tmp14
    tmp16 = tl.where(tmp15, tmp10, tmp13)
    tmp17 = float('nan')
    tmp18 = tmp16 == tmp17
    tmp19 = tl.where(tmp18, tmp10, tmp16)
    tl.store(in_out_ptr0 + x3, tmp19, xmask)


@triton.jit
def triton_poi_fused_diag_embed_index_put_lift_fresh_pow_2(in_out_ptr0,
    in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16
    x3 = xindex
    tmp3 = tl.load(in_ptr0 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (4 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (8 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp8 = tl.load(in_ptr0 + (12 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp0 = x0
    tmp1 = x1
    tmp2 = tmp0 == tmp1
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp9 = tmp7 + tmp8
    tmp10 = 0.0
    tmp11 = tl.where(tmp2, tmp9, tmp10)
    tmp12 = tl.full([1], 1, tl.int32)
    tmp13 = tmp12 / tmp11
    tmp14 = float('inf')
    tmp15 = tmp13 == tmp14
    tmp16 = tl.where(tmp15, tmp10, tmp13)
    tmp17 = float('nan')
    tmp18 = tmp16 == tmp17
    tmp19 = tl.where(tmp18, tmp10, tmp16)
    tl.store(in_out_ptr0 + x3, tmp19, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4,), (1,))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_abs_diag_embed_0[grid(16)](primals_1, buf0, buf1, 
            16, XBLOCK=16, num_warps=1, num_stages=1)
        del primals_1
        buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (16, 4), (4, 1), 0),
            buf1, out=buf2)
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf4 = buf3
        del buf3
        triton_poi_fused_diag_embed_index_put_lift_fresh_pow_1[grid(64)](buf4,
            buf2, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf5 = reinterpret_tensor(buf2, (4, 4, 4), (16, 4, 1), 0)
        del buf2
        buf6 = buf5
        del buf5
        triton_poi_fused_diag_embed_index_put_lift_fresh_pow_2[grid(64)](buf6,
            primals_2, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf7 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(buf4, primals_2, out=buf7)
        buf8 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf7, (16, 4), (4, 1), 0),
            buf1, out=buf8)
        del buf1
        buf9 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf8, (4, 4, 4), (16, 4, 1), 
            0), buf6, out=buf9)
        buf10 = reinterpret_tensor(buf8, (4, 4, 4), (16, 4, 1), 0)
        del buf8
        extern_kernels.bmm(buf9, reinterpret_tensor(primals_2, (4, 4, 4), (
            16, 1, 4), 0), out=buf10)
        buf11 = buf9
        del buf9
        extern_kernels.bmm(buf10, buf4, out=buf11)
        buf12 = buf10
        del buf10
        extern_kernels.bmm(buf11, primals_3, out=buf12)
        del buf11
    return buf12, buf0, buf0, reinterpret_tensor(primals_3, (4, 4, 4), (16,
        1, 4), 0), reinterpret_tensor(buf4, (4, 4, 4), (16, 1, 4), 0
        ), primals_2, reinterpret_tensor(buf6, (4, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(buf7, (4, 16), (1, 4), 0)


class HGCNNew(nn.Module):

    def __init__(self, n_edges, in_feature, out_feature, n_agents):
        super(HGCNNew, self).__init__()
        None
        self.W_line = nn.Parameter(torch.ones(n_edges))
        self.W = None

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