# 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/ti/ctighfmvx3fhiofuszhqc22eqpq4yxfe5zm4aqfvagwdj5pvefdz.py
# Topologically Sorted Source Nodes: [embed_input], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   embed_input => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_2, %expand, %sub, %mul], 2), 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=[256], 
    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': 6, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x3 = (xindex // 16)
    x1 = (xindex // 16) % 4
    x4 = 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*x3) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + ((4*x1) + ((-4) + x0)), tmp9 & xmask, eviction_policy='evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 12, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr0 + ((4*x3) + ((-8) + x0)), tmp14 & xmask, eviction_policy='evict_last', other=0.0)
    tmp16 = tl.load(in_ptr1 + ((4*x1) + ((-8) + x0)), tmp14 & xmask, eviction_policy='evict_last', other=0.0)
    tmp17 = tmp15 - tmp16
    tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
    tmp19 = tl.where(tmp14, tmp17, tmp18)
    tmp20 = tmp0 >= tmp12
    tmp21 = tl.full([1], 16, tl.int64)
    tmp22 = tmp0 < tmp21
    tmp23 = tl.load(in_ptr0 + ((4*x3) + ((-12) + x0)), tmp20 & xmask, eviction_policy='evict_last', other=0.0)
    tmp24 = tl.load(in_ptr1 + ((4*x1) + ((-12) + x0)), tmp20 & xmask, eviction_policy='evict_last', other=0.0)
    tmp25 = tmp23 * tmp24
    tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
    tmp27 = tl.where(tmp20, tmp25, tmp26)
    tmp28 = tl.where(tmp14, tmp19, tmp27)
    tmp29 = tl.where(tmp9, tmp10, tmp28)
    tmp30 = tl.where(tmp4, tmp5, tmp29)
    tl.store(out_ptr0 + (x4), tmp30, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/u7/cu7fu4tmqt3iqywrz3xktm46fuototooejk66dgew3xgnbgrf7x3.py
# Topologically Sorted Source Nodes: [output], Original ATen: [aten._prelu_kernel]
# Source node to ATen node mapping:
#   output => gt, mul_1, where
# Graph fragment:
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_1, 0), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_2, %view_1), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %view_1, %mul_1), kwargs = {})
triton_poi_fused__prelu_kernel_1 = async_compile.triton('triton_poi_fused__prelu_kernel_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=[512], 
    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__prelu_kernel_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__prelu_kernel_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp3 = tl.load(in_ptr1 + (0))
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp5 = tmp4 * tmp0
    tmp6 = tl.where(tmp2, tmp0, tmp5)
    tl.store(out_ptr0 + (x0), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/mt/cmtharafrhduy5nfmsodmdnhoi4x33a2coi3gv6xt2cp6rjqa7ff.py
# Topologically Sorted Source Nodes: [output_1], Original ATen: [aten.sigmoid]
# Source node to ATen node mapping:
#   output_1 => sigmoid
# Graph fragment:
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%view_4,), kwargs = {})
triton_poi_fused_sigmoid_2 = async_compile.triton('triton_poi_fused_sigmoid_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_sigmoid_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_sigmoid_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
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
    tmp1 = tl.load(in_ptr0 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = tl.sigmoid(tmp3)
    tl.store(in_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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (32, 16), (16, 1))
    assert_size_stride(primals_4, (32, ), (1, ))
    assert_size_stride(primals_5, (1, ), (1, ))
    assert_size_stride(primals_6, (1, 32), (32, 1))
    assert_size_stride(primals_7, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [embed_input], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(primals_2, primals_1, buf0, 256, grid=grid(256), stream=stream0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((16, 32), (32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_4, reinterpret_tensor(buf0, (16, 16), (16, 1), 0), reinterpret_tensor(primals_3, (16, 32), (1, 16), 0), alpha=1, beta=1, out=buf1)
        del primals_3
        del primals_4
        buf2 = empty_strided_cuda((4, 4, 32), (128, 32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [output], Original ATen: [aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_1.run(buf1, primals_5, buf2, 512, grid=grid(512), stream=stream0)
        buf3 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf2, (16, 32), (32, 1), 0), reinterpret_tensor(primals_6, (32, 1), (1, 32), 0), out=buf3)
        buf4 = reinterpret_tensor(buf3, (4, 4, 1), (4, 1, 1), 0); del buf3  # reuse
        # Topologically Sorted Source Nodes: [output_1], Original ATen: [aten.sigmoid]
        triton_poi_fused_sigmoid_2.run(buf4, primals_7, 16, grid=grid(16), stream=stream0)
        del primals_7
    return (buf4, primals_5, reinterpret_tensor(buf0, (16, 16), (16, 1), 0), buf1, reinterpret_tensor(buf2, (16, 32), (32, 1), 0), buf4, primals_6, )


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), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((32, 16), (16, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((1, 32), (32, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import torch
import torch.nn as nn
import torch.nn.functional as F


class Attention_ElementWiseProduct(nn.Module):
    """
      Input:
          behavior: 3D tensor with shape: ``(batch_size,field_size,embedding_size)``.
          candidate: 3D tensor with shape: ``(batch_size,1,embedding_size)``.
      Output:
          attention_weight: 3D tensor with shape: ``(batch_size, field_size, 1)``.
    """

    def __init__(self, embedding_size):
        super().__init__()
        self.linear1 = nn.Linear(4 * embedding_size, 32)
        self.linear2 = nn.Linear(32, 1)
        self.prelu = nn.PReLU()

    def forward(self, behavior, candidate):
        candidate = candidate.expand_as(behavior)
        embed_input = torch.cat([behavior, candidate, behavior - candidate,
            behavior * candidate], dim=2)
        output = self.prelu(self.linear1(embed_input))
        output = F.sigmoid(self.linear2(output))
        return output


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


def get_init_inputs():
    return [[], {'embedding_size': 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
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 = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x3 = xindex // 16
    x1 = xindex // 16 % 4
    x4 = 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 * x3 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp9 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 12, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr0 + (4 * x3 + (-8 + x0)), tmp14 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp16 = tl.load(in_ptr1 + (4 * x1 + (-8 + x0)), tmp14 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp17 = tmp15 - tmp16
    tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
    tmp19 = tl.where(tmp14, tmp17, tmp18)
    tmp20 = tmp0 >= tmp12
    tl.full([1], 16, tl.int64)
    tmp23 = tl.load(in_ptr0 + (4 * x3 + (-12 + x0)), tmp20 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp24 = tl.load(in_ptr1 + (4 * x1 + (-12 + x0)), tmp20 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp25 = tmp23 * tmp24
    tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
    tmp27 = tl.where(tmp20, tmp25, tmp26)
    tmp28 = tl.where(tmp14, tmp19, tmp27)
    tmp29 = tl.where(tmp9, tmp10, tmp28)
    tmp30 = tl.where(tmp4, tmp5, tmp29)
    tl.store(out_ptr0 + x4, tmp30, xmask)


@triton.jit
def triton_poi_fused__prelu_kernel_1(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp3 = tl.load(in_ptr1 + 0)
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp5 = tmp4 * tmp0
    tmp6 = tl.where(tmp2, tmp0, tmp5)
    tl.store(out_ptr0 + x0, tmp6, xmask)


@triton.jit
def triton_poi_fused_sigmoid_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
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = tl.sigmoid(tmp3)
    tl.store(in_out_ptr0 + x0, tmp4, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (32, 16), (16, 1))
    assert_size_stride(primals_4, (32,), (1,))
    assert_size_stride(primals_5, (1,), (1,))
    assert_size_stride(primals_6, (1, 32), (32, 1))
    assert_size_stride(primals_7, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(256)](primals_2, primals_1, buf0, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((16, 32), (32, 1), torch.float32)
        extern_kernels.addmm(primals_4, reinterpret_tensor(buf0, (16, 16),
            (16, 1), 0), reinterpret_tensor(primals_3, (16, 32), (1, 16), 0
            ), alpha=1, beta=1, out=buf1)
        del primals_3
        del primals_4
        buf2 = empty_strided_cuda((4, 4, 32), (128, 32, 1), torch.float32)
        triton_poi_fused__prelu_kernel_1[grid(512)](buf1, primals_5, buf2, 
            512, XBLOCK=128, num_warps=4, num_stages=1)
        buf3 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (16, 32), (32, 1), 0),
            reinterpret_tensor(primals_6, (32, 1), (1, 32), 0), out=buf3)
        buf4 = reinterpret_tensor(buf3, (4, 4, 1), (4, 1, 1), 0)
        del buf3
        triton_poi_fused_sigmoid_2[grid(16)](buf4, primals_7, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_7
    return buf4, primals_5, reinterpret_tensor(buf0, (16, 16), (16, 1), 0
        ), buf1, reinterpret_tensor(buf2, (16, 32), (32, 1), 0
        ), buf4, primals_6


class Attention_ElementWiseProductNew(nn.Module):
    """
      Input:
          behavior: 3D tensor with shape: ``(batch_size,field_size,embedding_size)``.
          candidate: 3D tensor with shape: ``(batch_size,1,embedding_size)``.
      Output:
          attention_weight: 3D tensor with shape: ``(batch_size, field_size, 1)``.
    """

    def __init__(self, embedding_size):
        super().__init__()
        self.linear1 = nn.Linear(4 * embedding_size, 32)
        self.linear2 = nn.Linear(32, 1)
        self.prelu = nn.PReLU()

    def forward(self, input_0, input_1):
        primals_3 = self.linear1.weight
        primals_4 = self.linear1.bias
        primals_6 = self.linear2.weight
        primals_5 = self.linear2.bias
        primals_7 = self.prelu.weight
        primals_2 = input_0
        primals_1 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]
