# 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_7/inductor_cache/hc/chc4ldrqcjtfxtm7z6bcimhl6kcxp5cg7bbybuqxykn7v4rbyrzr.py
# Topologically Sorted Source Nodes: [output_1, output_1_relu], Original ATen: [aten.add, aten.relu]
# Source node to ATen node mapping:
#   output_1 => add
#   output_1_relu => relu
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_1, %view_3), kwargs = {})
#   %relu : [num_users=3] = call_function[target=torch.ops.aten.relu.default](args = (%add,), kwargs = {})
triton_poi_fused_add_relu_0 = async_compile.triton('triton_poi_fused_add_relu_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_relu_0', '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_add_relu_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (x2), xmask)
    tmp4 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp7 = tl.full([1], 0, tl.int32)
    tmp8 = triton_helpers.maximum(tmp7, tmp6)
    tl.store(in_out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/jq/cjq2rka4eijqd5dhapwvei2kcb4mrciaxxt424vl3fene2a2d3mf.py
# Topologically Sorted Source Nodes: [output_2, output_2_res, output], Original ATen: [aten.add, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   output => relu_1
#   output_2 => add_1
#   output_2_res => add_2
# Graph fragment:
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_5, %view_7), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_1, %primals_3), kwargs = {})
#   %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_2,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_1, 0), kwargs = {})
triton_poi_fused_add_relu_threshold_backward_1 = async_compile.triton('triton_poi_fused_add_relu_threshold_backward_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: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*i1', 6: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_relu_threshold_backward_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 5, '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_relu_threshold_backward_1(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (x2), xmask)
    tmp4 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr3 + (x2), xmask)
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full([1], 0, tl.int32)
    tmp10 = triton_helpers.maximum(tmp9, tmp8)
    tmp11 = 0.0
    tmp12 = tmp10 <= tmp11
    tl.store(in_out_ptr0 + (x2), tmp10, xmask)
    tl.store(out_ptr0 + (x2), tmp12, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4, ), (1, ))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4, ), (1, ))
    assert_size_stride(primals_9, (4, 4), (4, 1))
    assert_size_stride(primals_10, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_3, (16, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [bmm], Original ATen: [aten.bmm]
        extern_kernels.bmm(primals_4, primals_3, out=buf1)
        buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf2)
        del primals_5
        buf3 = reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [output_1, output_1_relu], Original ATen: [aten.add, aten.relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_relu_0.run(buf3, primals_2, buf2, primals_6, 64, grid=grid(64), stream=stream0)
        del primals_2
        del primals_6
        buf4 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf3, (16, 4), (4, 1), 0), reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf4)
        buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [bmm_1], Original ATen: [aten.bmm]
        extern_kernels.bmm(primals_4, buf3, out=buf5)
        buf6 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf5, (16, 4), (4, 1), 0), reinterpret_tensor(primals_9, (4, 4), (1, 4), 0), out=buf6)
        buf7 = reinterpret_tensor(buf4, (4, 4, 4), (16, 4, 1), 0); del buf4  # reuse
        buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [output_2, output_2_res, output], Original ATen: [aten.add, aten.relu, aten.threshold_backward]
        triton_poi_fused_add_relu_threshold_backward_1.run(buf7, primals_8, buf6, primals_10, primals_3, buf8, 64, grid=grid(64), stream=stream0)
        del buf6
        del primals_10
        del primals_8
    return (buf7, reinterpret_tensor(primals_3, (16, 4), (4, 1), 0), reinterpret_tensor(buf1, (16, 4), (4, 1), 0), buf3, reinterpret_tensor(buf5, (16, 4), (4, 1), 0), buf8, primals_9, reinterpret_tensor(primals_4, (4, 4, 4), (16, 1, 4), 0), primals_7, )


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, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10])
    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
from torch import nn
import torch.autograd
from torch.nn.modules.module import Module


class GraphConvolution(Module):
    """
    Simple GCN layer, similar to https://arxiv.org/abs/1609.02907.
    """

    def __init__(self, state_dim, name='', out_state_dim=None):
        super(GraphConvolution, self).__init__()
        self.state_dim = state_dim
        if out_state_dim is None:
            self.out_state_dim = state_dim
        else:
            self.out_state_dim = out_state_dim
        self.fc1 = nn.Linear(in_features=self.state_dim, out_features=self.
            out_state_dim)
        self.fc2 = nn.Linear(in_features=self.state_dim, out_features=self.
            out_state_dim)
        self.name = name

    def forward(self, input, adj):
        state_in = self.fc1(input)
        forward_input = self.fc2(torch.bmm(adj, input))
        return state_in + forward_input


class GraphResConvolution(Module):
    """
    Simple GCN layer, similar to https://arxiv.org/abs/1609.02907.
    """

    def __init__(self, state_dim, name=''):
        super(GraphResConvolution, self).__init__()
        self.state_dim = state_dim
        self.gcn_1 = GraphConvolution(state_dim, f'{name}_1')
        self.gcn_2 = GraphConvolution(state_dim, f'{name}_2')
        self.relu1 = nn.ReLU()
        self.relu2 = nn.ReLU()
        self.name = name

    def forward(self, input, adj):
        output_1 = self.gcn_1(input, adj)
        output_1_relu = self.relu1(output_1)
        output_2 = self.gcn_2(output_1_relu, adj)
        output_2_res = output_2 + input
        output = self.relu2(output_2_res)
        return output


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


def get_init_inputs():
    return [[], {'state_dim': 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 import triton_helpers
from torch.nn import Module
from torch import nn
import torch.autograd
from torch.nn.modules.module import Module
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_add_relu_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x2, xmask)
    tmp4 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp7 = tl.full([1], 0, tl.int32)
    tmp8 = triton_helpers.maximum(tmp7, tmp6)
    tl.store(in_out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_add_relu_threshold_backward_1(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x2, xmask)
    tmp4 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr3 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp8 = tmp6 + tmp7
    tmp9 = tl.full([1], 0, tl.int32)
    tmp10 = triton_helpers.maximum(tmp9, tmp8)
    tmp11 = 0.0
    tmp12 = tmp10 <= tmp11
    tl.store(in_out_ptr0 + x2, tmp10, xmask)
    tl.store(out_ptr0 + x2, tmp12, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4, 4), (4, 1))
    assert_size_stride(primals_10, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(primals_4, primals_3, out=buf1)
        buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf2)
        del primals_5
        buf3 = reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_add_relu_0[grid(64)](buf3, primals_2, buf2,
            primals_6, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_2
        del primals_6
        buf4 = buf2
        del buf2
        extern_kernels.mm(reinterpret_tensor(buf3, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf4)
        buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(primals_4, buf3, out=buf5)
        buf6 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf5, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_9, (4, 4), (1, 4), 0), out=buf6)
        buf7 = reinterpret_tensor(buf4, (4, 4, 4), (16, 4, 1), 0)
        del buf4
        buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        triton_poi_fused_add_relu_threshold_backward_1[grid(64)](buf7,
            primals_8, buf6, primals_10, primals_3, buf8, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del buf6
        del primals_10
        del primals_8
    return buf7, reinterpret_tensor(primals_3, (16, 4), (4, 1), 0
        ), reinterpret_tensor(buf1, (16, 4), (4, 1), 0
        ), buf3, reinterpret_tensor(buf5, (16, 4), (4, 1), 0
        ), buf8, primals_9, reinterpret_tensor(primals_4, (4, 4, 4), (16, 1,
        4), 0), primals_7


class GraphConvolution(Module):
    """
    Simple GCN layer, similar to https://arxiv.org/abs/1609.02907.
    """

    def __init__(self, state_dim, name='', out_state_dim=None):
        super(GraphConvolution, self).__init__()
        self.state_dim = state_dim
        if out_state_dim is None:
            self.out_state_dim = state_dim
        else:
            self.out_state_dim = out_state_dim
        self.fc1 = nn.Linear(in_features=self.state_dim, out_features=self.
            out_state_dim)
        self.fc2 = nn.Linear(in_features=self.state_dim, out_features=self.
            out_state_dim)
        self.name = name

    def forward(self, input, adj):
        state_in = self.fc1(input)
        forward_input = self.fc2(torch.bmm(adj, input))
        return state_in + forward_input


class GraphResConvolutionNew(Module):
    """
    Simple GCN layer, similar to https://arxiv.org/abs/1609.02907.
    """

    def __init__(self, state_dim, name=''):
        super(GraphResConvolutionNew, self).__init__()
        self.state_dim = state_dim
        self.gcn_1 = GraphConvolution(state_dim, f'{name}_1')
        self.gcn_2 = GraphConvolution(state_dim, f'{name}_2')
        self.relu1 = nn.ReLU()
        self.relu2 = nn.ReLU()
        self.name = name

    def forward(self, input_0, input_1):
        primals_1 = self.gcn_1.fc1.weight
        primals_2 = self.gcn_1.fc1.bias
        primals_5 = self.gcn_1.fc2.weight
        primals_6 = self.gcn_1.fc2.bias
        primals_7 = self.gcn_2.fc1.weight
        primals_8 = self.gcn_2.fc1.bias
        primals_9 = self.gcn_2.fc2.weight
        primals_10 = self.gcn_2.fc2.bias
        primals_3 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9, primals_10])
        return output[0]
