# 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_8/inductor_cache/ms/cmsuzohbg5nq52jnvirovzkvykrzzko5xomu7zyu5e5u2lhegppw.py
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   cat => cat
# Graph fragment:
#   %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_1, %primals_2], -1), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_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=[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_0', '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_0(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.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + (x2), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/2k/c2kiox2wvshockbbzjlycxwhjeigavlrfwuvcpbcbxpipbm7d7k6.py
# Topologically Sorted Source Nodes: [input_2], Original ATen: [aten.tanh]
# Source node to ATen node mapping:
#   input_2 => tanh
# Graph fragment:
#   %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_4), kwargs = {})
#   %tanh : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%add_tensor,), kwargs = {})
triton_poi_fused_tanh_1 = async_compile.triton('triton_poi_fused_tanh_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_tanh_1', '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_tanh_1(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 = libdevice.tanh(tmp2)
    tl.store(in_out_ptr0 + (x2), tmp3, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/q2/cq2dva75ywvhmyncgbl34k6u5idvlnynw45n325heyfgsgie4cxz.py
# Topologically Sorted Source Nodes: [input_5, getitem_1, getitem_3], Original ATen: [aten.tanh, aten.index]
# Source node to ATen node mapping:
#   getitem_1 => index
#   getitem_3 => index_1
#   input_5 => tanh_1
# Graph fragment:
#   %tanh_1 : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%addmm_2,), kwargs = {})
#   %index : [num_users=1] = call_function[target=torch.ops.aten.index.Tensor](args = (%tanh_1, [%select]), kwargs = {})
#   %index_1 : [num_users=1] = call_function[target=torch.ops.aten.index.Tensor](args = (%tanh_1, [%select_1]), kwargs = {})
triton_poi_fused_index_tanh_2 = async_compile.triton('triton_poi_fused_index_tanh_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: '*i64', 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_index_tanh_2', '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_index_tanh_2(in_ptr0, in_ptr1, 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
    x1 = (xindex // 4)
    x0 = xindex % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 4, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert(((0 <= tmp4) & (tmp4 < 4)) | ~(xmask), "index out of bounds: 0 <= tmp4 < 4")
    tmp6 = tl.load(in_ptr1 + (x0 + (4*tmp4)), xmask)
    tmp7 = libdevice.tanh(tmp6)
    tmp9 = tmp8 + tmp1
    tmp10 = tmp8 < 0
    tmp11 = tl.where(tmp10, tmp9, tmp8)
    tl.device_assert(((0 <= tmp11) & (tmp11 < 4)) | ~(xmask), "index out of bounds: 0 <= tmp11 < 4")
    tmp13 = tl.load(in_ptr1 + (x0 + (4*tmp11)), xmask)
    tmp14 = libdevice.tanh(tmp13)
    tl.store(out_ptr0 + (x2), tmp7, xmask)
    tl.store(out_ptr1 + (x2), tmp14, 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, 8), (8, 1))
    assert_size_stride(primals_4, (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, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(primals_1, primals_2, buf0, 32, grid=grid(32), stream=stream0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf0, reinterpret_tensor(primals_3, (8, 4), (1, 8), 0), out=buf1)
        del primals_3
        buf2 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [input_2], Original ATen: [aten.tanh]
        triton_poi_fused_tanh_1.run(buf2, primals_4, 16, grid=grid(16), stream=stream0)
        del primals_4
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_3], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_6, buf2, reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf3)
        del primals_6
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_4], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_8, buf3, reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf4)
        del primals_8
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_5, getitem_1, getitem_3], Original ATen: [aten.tanh, aten.index]
        triton_poi_fused_index_tanh_2.run(primals_9, buf4, buf5, buf6, 16, grid=grid(16), stream=stream0)
        buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_5, getitem_1, getitem_3, new_state], Original ATen: [aten.tanh, aten.index, aten.add]
        extern_kernels._mm_plus_mm(primals_10, buf5, primals_10, buf6, out=buf7)
        del buf5
        del buf6
    return (buf7, buf0, buf2, buf3, buf4, reinterpret_tensor(primals_9, (4, ), (4, ), 0), reinterpret_tensor(primals_9, (4, ), (4, ), 1), reinterpret_tensor(primals_10, (4, 4), (1, 4), 0), primals_7, primals_5, )


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, 8), (8, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((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.int64)
    primals_10 = rand_strided((4, 4), (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 typing
import torch.nn as nn


class MLP(nn.Module):

    def __init__(self, input_dim, hidden_sizes: 'typing.Iterable[int]',
        out_dim, activation_function=nn.Sigmoid(), activation_out=None):
        super(MLP, self).__init__()
        i_h_sizes = [input_dim] + hidden_sizes
        self.mlp = nn.Sequential()
        for idx in range(len(i_h_sizes) - 1):
            self.mlp.add_module('layer_{}'.format(idx), nn.Linear(
                in_features=i_h_sizes[idx], out_features=i_h_sizes[idx + 1]))
            self.mlp.add_module('act', activation_function)
        self.mlp.add_module('out_layer', nn.Linear(i_h_sizes[-1], out_dim))
        if activation_out is not None:
            self.mlp.add_module('out_layer_activation', activation_out)

    def init(self):
        for i, l in enumerate(self.mlp):
            if type(l) == nn.Linear:
                nn.init.xavier_normal_(l.weight)

    def forward(self, x):
        return self.mlp(x)


class GINPreTransition(nn.Module):

    def __init__(self, node_state_dim: 'int', node_label_dim: 'int',
        mlp_hidden_dim: 'typing.Iterable[int]', activation_function=nn.Tanh()):
        super(type(self), self).__init__()
        d_i = node_state_dim + node_label_dim
        d_o = node_state_dim
        d_h = list(mlp_hidden_dim)
        self.mlp = MLP(input_dim=d_i, hidden_sizes=d_h, out_dim=d_o,
            activation_function=activation_function, activation_out=
            activation_function)

    def forward(self, node_states, node_labels, edges, agg_matrix):
        intermediate_states = self.mlp(torch.cat([node_states, node_labels],
            -1))
        new_state = torch.matmul(agg_matrix, intermediate_states[edges[:, 0]]
            ) + torch.matmul(agg_matrix, intermediate_states[edges[:, 1]])
        return new_state


def get_inputs():
    return [torch.rand([4, 4]), torch.rand([4, 4]), torch.ones([4, 4],
        dtype=torch.int64), torch.rand([4, 4])]


def get_init_inputs():
    return [[], {'node_state_dim': 4, 'node_label_dim': 4, 'mlp_hidden_dim':
        [4, 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 typing
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_cat_0(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.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_tanh_1(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 = libdevice.tanh(tmp2)
    tl.store(in_out_ptr0 + x2, tmp3, xmask)


@triton.jit
def triton_poi_fused_index_tanh_2(in_ptr0, in_ptr1, 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
    x1 = xindex // 4
    x0 = xindex % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 4, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert((0 <= tmp4) & (tmp4 < 4) | ~xmask,
        'index out of bounds: 0 <= tmp4 < 4')
    tmp6 = tl.load(in_ptr1 + (x0 + 4 * tmp4), xmask)
    tmp7 = libdevice.tanh(tmp6)
    tmp9 = tmp8 + tmp1
    tmp10 = tmp8 < 0
    tmp11 = tl.where(tmp10, tmp9, tmp8)
    tl.device_assert((0 <= tmp11) & (tmp11 < 4) | ~xmask,
        'index out of bounds: 0 <= tmp11 < 4')
    tmp13 = tl.load(in_ptr1 + (x0 + 4 * tmp11), xmask)
    tmp14 = libdevice.tanh(tmp13)
    tl.store(out_ptr0 + x2, tmp7, xmask)
    tl.store(out_ptr1 + x2, tmp14, 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, 8), (8, 1))
    assert_size_stride(primals_4, (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, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(32)](primals_1, primals_2, buf0, 32,
            XBLOCK=32, num_warps=1, num_stages=1)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf0, reinterpret_tensor(primals_3, (8, 4), (1, 8
            ), 0), out=buf1)
        del primals_3
        buf2 = buf1
        del buf1
        triton_poi_fused_tanh_1[grid(16)](buf2, primals_4, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_4
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_6, buf2, reinterpret_tensor(primals_5,
            (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf3)
        del primals_6
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_8, buf3, reinterpret_tensor(primals_7,
            (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf4)
        del primals_8
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_index_tanh_2[grid(16)](primals_9, buf4, buf5, buf6,
            16, XBLOCK=16, num_warps=1, num_stages=1)
        buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels._mm_plus_mm(primals_10, buf5, primals_10, buf6, out=buf7
            )
        del buf5
        del buf6
    return buf7, buf0, buf2, buf3, buf4, reinterpret_tensor(primals_9, (4,),
        (4,), 0), reinterpret_tensor(primals_9, (4,), (4,), 1
        ), reinterpret_tensor(primals_10, (4, 4), (1, 4), 0
        ), primals_7, primals_5


class MLP(nn.Module):

    def __init__(self, input_dim, hidden_sizes: 'typing.Iterable[int]',
        out_dim, activation_function=nn.Sigmoid(), activation_out=None):
        super(MLP, self).__init__()
        i_h_sizes = [input_dim] + hidden_sizes
        self.mlp = nn.Sequential()
        for idx in range(len(i_h_sizes) - 1):
            self.mlp.add_module('layer_{}'.format(idx), nn.Linear(
                in_features=i_h_sizes[idx], out_features=i_h_sizes[idx + 1]))
            self.mlp.add_module('act', activation_function)
        self.mlp.add_module('out_layer', nn.Linear(i_h_sizes[-1], out_dim))
        if activation_out is not None:
            self.mlp.add_module('out_layer_activation', activation_out)

    def init(self):
        for i, l in enumerate(self.mlp):
            if type(l) == nn.Linear:
                nn.init.xavier_normal_(l.weight)

    def forward(self, x):
        return self.mlp(x)


class GINPreTransitionNew(nn.Module):

    def __init__(self, node_state_dim: 'int', node_label_dim: 'int',
        mlp_hidden_dim: 'typing.Iterable[int]', activation_function=nn.Tanh()):
        super(type(self), self).__init__()
        d_i = node_state_dim + node_label_dim
        d_o = node_state_dim
        d_h = list(mlp_hidden_dim)
        self.mlp = MLP(input_dim=d_i, hidden_sizes=d_h, out_dim=d_o,
            activation_function=activation_function, activation_out=
            activation_function)

    def forward(self, input_0, input_1, input_2, input_3):
        primals_3 = self.mlp.mlp.layer_0.weight
        primals_4 = self.mlp.mlp.layer_0.bias
        primals_1 = self.mlp.mlp.layer_1.weight
        primals_6 = self.mlp.mlp.layer_1.bias
        primals_2 = self.mlp.mlp.out_layer.weight
        primals_8 = self.mlp.mlp.out_layer.bias
        primals_5 = input_0
        primals_7 = input_1
        primals_9 = input_2
        primals_10 = input_3
        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]
