# 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/3w/c3w6ed26vbtzgzigkkyuiuwphm777x7aoogcxkvoly3ia7qifnzg.py
# Topologically Sorted Source Nodes: [embeddings_1], Original ATen: [aten.relu]
# Source node to ATen node mapping:
#   embeddings_1 => relu
# Graph fragment:
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%mm_1,), kwargs = {})
triton_poi_fused_relu_0 = async_compile.triton('triton_poi_fused_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=[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_relu_0', '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_relu_0(in_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
    tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(in_out_ptr0 + (x0), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/fw/cfweytfxnso6abkha6rk5gwco2kbvibvw4vuddh56meprt4wzlj6.py
# Topologically Sorted Source Nodes: [combined_rep], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   combined_rep => cat
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_2, %relu_1], 1), kwargs = {})
triton_poi_fused_cat_1 = async_compile.triton('triton_poi_fused_cat_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=[32], 
    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_cat_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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_cat_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = (xindex // 8)
    x2 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + ((4*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + ((4*x1) + ((-4) + x0)), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
    tmp10 = tl.full([1], 0, tl.int32)
    tmp11 = triton_helpers.maximum(tmp10, tmp9)
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp6, tmp11, tmp12)
    tmp14 = tl.where(tmp4, tmp5, tmp13)
    tl.store(out_ptr0 + (x2), tmp14, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/nc/cncd5yvgjpjfrvyaveva55remy3ckx5quvuhdmurdt3pp6k3qtux.py
# Topologically Sorted Source Nodes: [hidden_rep], Original ATen: [aten.relu]
# Source node to ATen node mapping:
#   hidden_rep => relu_2
# Graph fragment:
#   %add_tensor_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_1, %primals_6), kwargs = {})
#   %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor_1,), kwargs = {})
triton_poi_fused_relu_2 = async_compile.triton('triton_poi_fused_relu_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=[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_relu_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_relu_2(in_out_ptr0, in_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_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x2), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/bh/cbhtmvezuerduixfmmlufdezlxhnkrnst5yyfg6jm4v6w6pkgfrl.py
# Topologically Sorted Source Nodes: [logits], Original ATen: [aten.mean]
# Source node to ATen node mapping:
#   logits => mean
# Graph fragment:
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%addmm_2, [0]), kwargs = {})
triton_poi_fused_mean_3 = async_compile.triton('triton_poi_fused_mean_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=[4], 
    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_mean_3', '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_mean_3(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    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.load(in_ptr0 + (4 + x0), xmask)
    tmp3 = tl.load(in_ptr0 + (8 + x0), xmask)
    tmp5 = tl.load(in_ptr0 + (12 + x0), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tl.store(out_ptr0 + (x0), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/cs/ccserabwgg7d2qqq373bkl66yqoopjygng3rto5goox6mh6ekjmx.py
# Topologically Sorted Source Nodes: [embeddings_4], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   embeddings_4 => relu_1
# Graph fragment:
#   %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%mm_3,), kwargs = {})
#   %le_2 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_1, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_4 = async_compile.triton('triton_poi_fused_relu_threshold_backward_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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i1', 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_relu_threshold_backward_4', '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_relu_threshold_backward_4(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
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp3 = 0.0
    tmp4 = tmp2 <= tmp3
    tl.store(out_ptr0 + (x0), tmp4, 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, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4, 8), (8, 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((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [support], Original ATen: [aten.mm]
        extern_kernels.mm(primals_2, primals_1, out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [embeddings], Original ATen: [aten.mm]
        extern_kernels.mm(primals_3, buf0, out=buf1)
        buf2 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [embeddings_1], Original ATen: [aten.relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_relu_0.run(buf2, 16, grid=grid(16), stream=stream0)
        buf3 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [support_1], Original ATen: [aten.mm]
        extern_kernels.mm(buf2, primals_4, out=buf3)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [embeddings_3], Original ATen: [aten.mm]
        extern_kernels.mm(primals_3, buf3, out=buf4)
        buf5 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [combined_rep], Original ATen: [aten.cat]
        triton_poi_fused_cat_1.run(primals_2, buf4, buf5, 32, grid=grid(32), stream=stream0)
        buf6 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf5, reinterpret_tensor(primals_5, (8, 4), (1, 8), 0), out=buf6)
        buf7 = buf6; del buf6  # reuse
        # Topologically Sorted Source Nodes: [hidden_rep], Original ATen: [aten.relu]
        triton_poi_fused_relu_2.run(buf7, primals_6, 16, grid=grid(16), stream=stream0)
        del primals_6
        buf8 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf7, reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf8)
        buf9 = buf8; del buf8  # reuse
        # Topologically Sorted Source Nodes: [graph_rep], Original ATen: [aten.relu]
        triton_poi_fused_relu_2.run(buf9, primals_8, 16, grid=grid(16), stream=stream0)
        del primals_8
        buf10 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_2], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_10, buf9, reinterpret_tensor(primals_9, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf10)
        del primals_10
        buf11 = empty_strided_cuda((4, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [logits], Original ATen: [aten.mean]
        triton_poi_fused_mean_3.run(buf10, buf11, 4, grid=grid(4), stream=stream0)
        del buf10
        buf12 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [embeddings_4], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_4.run(buf4, buf12, 16, grid=grid(16), stream=stream0)
        del buf4
    return (buf11, buf2, buf5, buf7, buf9, primals_9, primals_7, primals_5, buf12, reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), reinterpret_tensor(primals_2, (4, 4), (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((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, 8), (8, 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)

import torch
import torch.nn as nn
import torch.utils.data


class Readout(nn.Module):
    """
    This module learns a single graph level representation for a molecule given GraphSAGE generated embeddings
    """

    def __init__(self, attr_dim, embedding_dim, hidden_dim, output_dim,
        num_cats):
        super(Readout, self).__init__()
        self.attr_dim = attr_dim
        self.hidden_dim = hidden_dim
        self.output_dim = output_dim
        self.num_cats = num_cats
        self.layer1 = nn.Linear(attr_dim + embedding_dim, hidden_dim)
        self.layer2 = nn.Linear(hidden_dim, output_dim)
        self.output = nn.Linear(output_dim, num_cats)
        self.act = nn.ReLU()

    def forward(self, node_features, node_embeddings):
        combined_rep = torch.cat((node_features, node_embeddings), dim=1)
        hidden_rep = self.act(self.layer1(combined_rep))
        graph_rep = self.act(self.layer2(hidden_rep))
        logits = torch.mean(self.output(graph_rep), dim=0)
        return logits


class GCN(nn.Module):
    """
    Graph Convolutional Network based on https://arxiv.org/abs/1609.02907

    """

    def __init__(self, feat_dim, hidden_dim1, hidden_dim2, dropout,
        is_sparse=False):
        """Dense version of GAT."""
        super(GCN, self).__init__()
        self.W1 = nn.Parameter(torch.FloatTensor(feat_dim, hidden_dim1))
        self.W2 = nn.Parameter(torch.FloatTensor(hidden_dim1, hidden_dim2))
        self.relu = nn.ReLU()
        self.dropout = nn.Dropout(p=dropout)
        nn.init.xavier_uniform_(self.W1.data)
        nn.init.xavier_uniform_(self.W2.data)
        self.is_sparse = is_sparse

    def forward(self, x, adj):
        support = torch.mm(x, self.W1)
        embeddings = torch.sparse.mm(adj, support
            ) if self.is_sparse else torch.mm(adj, support)
        embeddings = self.relu(embeddings)
        embeddings = self.dropout(embeddings)
        support = torch.mm(embeddings, self.W2)
        embeddings = torch.sparse.mm(adj, support
            ) if self.is_sparse else torch.mm(adj, support)
        embeddings = self.relu(embeddings)
        return embeddings


class GCNClassification(nn.Module):
    """
    Network that consolidates GCN + Readout into a single nn.Module
    """

    def __init__(self, feat_dim, hidden_dim, node_embedding_dim, dropout,
        readout_hidden_dim, graph_embedding_dim, num_categories, sparse_adj
        =False):
        super(GCNClassification, self).__init__()
        self.gcn = GCN(feat_dim, hidden_dim, node_embedding_dim, dropout,
            is_sparse=sparse_adj)
        self.readout = Readout(feat_dim, node_embedding_dim,
            readout_hidden_dim, graph_embedding_dim, num_categories)

    def forward(self, adj_matrix, feature_matrix):
        node_embeddings = self.gcn(feature_matrix, adj_matrix)
        logits = self.readout(feature_matrix, node_embeddings)
        return logits


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


def get_init_inputs():
    return [[], {'feat_dim': 4, 'hidden_dim': 4, 'node_embedding_dim': 4,
        'dropout': 0.5, 'readout_hidden_dim': 4, 'graph_embedding_dim': 4,
        'num_categories': 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
import torch.nn as nn
import torch.utils.data
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_relu_0(in_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
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(in_out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = xindex // 8
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.full([1], 0, tl.int32)
    tmp11 = triton_helpers.maximum(tmp10, tmp9)
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp6, tmp11, tmp12)
    tmp14 = tl.where(tmp4, tmp5, tmp13)
    tl.store(out_ptr0 + x2, tmp14, xmask)


@triton.jit
def triton_poi_fused_relu_2(in_out_ptr0, in_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_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_mean_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    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.load(in_ptr0 + (4 + x0), xmask)
    tmp3 = tl.load(in_ptr0 + (8 + x0), xmask)
    tmp5 = tl.load(in_ptr0 + (12 + x0), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tl.store(out_ptr0 + x0, tmp8, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_4(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
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp3 = 0.0
    tmp4 = tmp2 <= tmp3
    tl.store(out_ptr0 + x0, tmp4, 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, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4, 8), (8, 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((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_2, primals_1, out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_3, buf0, out=buf1)
        buf2 = buf1
        del buf1
        get_raw_stream(0)
        triton_poi_fused_relu_0[grid(16)](buf2, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        buf3 = buf0
        del buf0
        extern_kernels.mm(buf2, primals_4, out=buf3)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_3, buf3, out=buf4)
        buf5 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        triton_poi_fused_cat_1[grid(32)](primals_2, buf4, buf5, 32, XBLOCK=
            32, num_warps=1, num_stages=1)
        buf6 = buf3
        del buf3
        extern_kernels.mm(buf5, reinterpret_tensor(primals_5, (8, 4), (1, 8
            ), 0), out=buf6)
        buf7 = buf6
        del buf6
        triton_poi_fused_relu_2[grid(16)](buf7, primals_6, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_6
        buf8 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf7, reinterpret_tensor(primals_7, (4, 4), (1, 4
            ), 0), out=buf8)
        buf9 = buf8
        del buf8
        triton_poi_fused_relu_2[grid(16)](buf9, primals_8, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_8
        buf10 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_10, buf9, reinterpret_tensor(primals_9,
            (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf10)
        del primals_10
        buf11 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_poi_fused_mean_3[grid(4)](buf10, buf11, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        del buf10
        buf12 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_4[grid(16)](buf4, buf12, 
            16, XBLOCK=16, num_warps=1, num_stages=1)
        del buf4
    return (buf11, buf2, buf5, buf7, buf9, primals_9, primals_7, primals_5,
        buf12, reinterpret_tensor(primals_3, (4, 4), (1, 4), 0),
        reinterpret_tensor(primals_4, (4, 4), (1, 4), 0),
        reinterpret_tensor(primals_2, (4, 4), (1, 4), 0))


class Readout(nn.Module):
    """
    This module learns a single graph level representation for a molecule given GraphSAGE generated embeddings
    """

    def __init__(self, attr_dim, embedding_dim, hidden_dim, output_dim,
        num_cats):
        super(Readout, self).__init__()
        self.attr_dim = attr_dim
        self.hidden_dim = hidden_dim
        self.output_dim = output_dim
        self.num_cats = num_cats
        self.layer1 = nn.Linear(attr_dim + embedding_dim, hidden_dim)
        self.layer2 = nn.Linear(hidden_dim, output_dim)
        self.output = nn.Linear(output_dim, num_cats)
        self.act = nn.ReLU()

    def forward(self, node_features, node_embeddings):
        combined_rep = torch.cat((node_features, node_embeddings), dim=1)
        hidden_rep = self.act(self.layer1(combined_rep))
        graph_rep = self.act(self.layer2(hidden_rep))
        logits = torch.mean(self.output(graph_rep), dim=0)
        return logits


class GCN(nn.Module):
    """
    Graph Convolutional Network based on https://arxiv.org/abs/1609.02907

    """

    def __init__(self, feat_dim, hidden_dim1, hidden_dim2, dropout,
        is_sparse=False):
        """Dense version of GAT."""
        super(GCN, self).__init__()
        self.W1 = nn.Parameter(torch.FloatTensor(feat_dim, hidden_dim1))
        self.W2 = nn.Parameter(torch.FloatTensor(hidden_dim1, hidden_dim2))
        self.relu = nn.ReLU()
        self.dropout = nn.Dropout(p=dropout)
        nn.init.xavier_uniform_(self.W1.data)
        nn.init.xavier_uniform_(self.W2.data)
        self.is_sparse = is_sparse

    def forward(self, x, adj):
        support = torch.mm(x, self.W1)
        embeddings = torch.sparse.mm(adj, support
            ) if self.is_sparse else torch.mm(adj, support)
        embeddings = self.relu(embeddings)
        embeddings = self.dropout(embeddings)
        support = torch.mm(embeddings, self.W2)
        embeddings = torch.sparse.mm(adj, support
            ) if self.is_sparse else torch.mm(adj, support)
        embeddings = self.relu(embeddings)
        return embeddings


class GCNClassificationNew(nn.Module):
    """
    Network that consolidates GCN + Readout into a single nn.Module
    """

    def __init__(self, feat_dim, hidden_dim, node_embedding_dim, dropout,
        readout_hidden_dim, graph_embedding_dim, num_categories, sparse_adj
        =False):
        super(GCNClassificationNew, self).__init__()
        self.gcn = GCN(feat_dim, hidden_dim, node_embedding_dim, dropout,
            is_sparse=sparse_adj)
        self.readout = Readout(feat_dim, node_embedding_dim,
            readout_hidden_dim, graph_embedding_dim, num_categories)

    def forward(self, input_0, input_1):
        primals_1 = self.gcn.W1
        primals_2 = self.gcn.W2
        primals_5 = self.readout.layer1.weight
        primals_6 = self.readout.layer1.bias
        primals_3 = self.readout.layer2.weight
        primals_8 = self.readout.layer2.bias
        primals_4 = self.readout.output.weight
        primals_10 = self.readout.output.bias
        primals_7 = input_0
        primals_9 = 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]
