# 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/35/c35ujvx5bel7vpowl4hqzjwki2sb6lxgnryizhxoaziad3wfj6e4.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten._prelu_kernel]
# Source node to ATen node mapping:
#   x => gt, mul, where
# Graph fragment:
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_1, 0), kwargs = {})
#   %mul : [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), kwargs = {})
triton_poi_fused__prelu_kernel_0 = async_compile.triton('triton_poi_fused__prelu_kernel_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=[262144], 
    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_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__prelu_kernel_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 262144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), None)
    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, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/3d/c3duavnlkf2v5tcto4gzomtyqh3egvwrrgelcxruvhveva5qjc46.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten._prelu_kernel]
# Source node to ATen node mapping:
#   x_1 => gt_1, mul_1, where_1
# Graph fragment:
#   %gt_1 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_4, 0), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_5, %view_4), kwargs = {})
#   %where_1 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %view_4, %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=[131072], 
    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 = 131072
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), None)
    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, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/bb/cbbsri6rycyl22i5j44dtskxt3m2bh7s5aiotjqtshfnkfdqqt3z.py
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten._prelu_kernel]
# Source node to ATen node mapping:
#   x_2 => gt_2, mul_2, where_2
# Graph fragment:
#   %gt_2 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_7, 0), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_8, %view_7), kwargs = {})
#   %where_2 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_2, %view_7, %mul_2), kwargs = {})
triton_poi_fused__prelu_kernel_2 = async_compile.triton('triton_poi_fused__prelu_kernel_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=[65536], 
    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_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__prelu_kernel_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 65536
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), None)
    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, None)
''', 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, primals_11, primals_12 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4096, 4), (4, 1))
    assert_size_stride(primals_3, (4096, ), (1, ))
    assert_size_stride(primals_4, (1, ), (1, ))
    assert_size_stride(primals_5, (2048, 4096), (4096, 1))
    assert_size_stride(primals_6, (2048, ), (1, ))
    assert_size_stride(primals_7, (1, ), (1, ))
    assert_size_stride(primals_8, (1024, 2048), (2048, 1))
    assert_size_stride(primals_9, (1024, ), (1, ))
    assert_size_stride(primals_10, (1, ), (1, ))
    assert_size_stride(primals_11, (4, 1024), (1024, 1))
    assert_size_stride(primals_12, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4096), (4096, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_3, reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4096), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del primals_2
        del primals_3
        buf1 = empty_strided_cuda((4, 4, 4, 4096), (65536, 16384, 4096, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten._prelu_kernel]
        stream0 = get_raw_stream(0)
        triton_poi_fused__prelu_kernel_0.run(buf0, primals_4, buf1, 262144, grid=grid(262144), stream=stream0)
        buf2 = empty_strided_cuda((64, 2048), (2048, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_6, reinterpret_tensor(buf1, (64, 4096), (4096, 1), 0), reinterpret_tensor(primals_5, (4096, 2048), (1, 4096), 0), alpha=1, beta=1, out=buf2)
        del primals_6
        buf3 = empty_strided_cuda((4, 4, 4, 2048), (32768, 8192, 2048, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_1.run(buf2, primals_7, buf3, 131072, grid=grid(131072), stream=stream0)
        buf4 = empty_strided_cuda((64, 1024), (1024, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_2], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_9, reinterpret_tensor(buf3, (64, 2048), (2048, 1), 0), reinterpret_tensor(primals_8, (2048, 1024), (1, 2048), 0), alpha=1, beta=1, out=buf4)
        del primals_9
        buf5 = empty_strided_cuda((4, 4, 4, 1024), (16384, 4096, 1024, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten._prelu_kernel]
        triton_poi_fused__prelu_kernel_2.run(buf4, primals_10, buf5, 65536, grid=grid(65536), stream=stream0)
        buf6 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_3], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_12, reinterpret_tensor(buf5, (64, 1024), (1024, 1), 0), reinterpret_tensor(primals_11, (1024, 4), (1, 1024), 0), alpha=1, beta=1, out=buf6)
        del primals_12
    return (reinterpret_tensor(buf6, (4, 4, 4, 4), (64, 16, 4, 1), 0), primals_4, primals_7, primals_10, reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), buf0, reinterpret_tensor(buf1, (64, 4096), (4096, 1), 0), buf2, reinterpret_tensor(buf3, (64, 2048), (2048, 1), 0), buf4, reinterpret_tensor(buf5, (64, 1024), (1024, 1), 0), primals_11, primals_8, 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, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4096, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4096, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((2048, 4096), (4096, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((2048, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((1024, 2048), (2048, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((1024, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((4, 1024), (1024, 1), device='cuda:0', dtype=torch.float32)
    primals_12 = 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, primals_11, primals_12])
    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 _paritybench_helpers import _mock_config
import torch
from torch import nn


class CustomClassificationHead(nn.Module):

    def __init__(self, config, input_dim, n_labels):
        super().__init__()
        self.config = config
        self.fc1 = nn.Linear(input_dim, 4096)
        self.fc2 = nn.Linear(4096, 2048)
        self.fc3 = nn.Linear(2048, 1024)
        self.fc4 = nn.Linear(1024, n_labels)
        self.dropout = nn.Dropout(p=0.3)
        self.prelu1 = nn.PReLU()
        self.prelu2 = nn.PReLU()
        self.prelu3 = nn.PReLU()
        nn.init.kaiming_normal_(self.fc1.weight)
        nn.init.kaiming_normal_(self.fc2.weight)
        nn.init.kaiming_normal_(self.fc3.weight)
        nn.init.kaiming_normal_(self.fc4.weight)

    def forward(self, x):
        if len(x.size()) == 3:
            x = x[:, 0, :]
        x = self.prelu1(self.fc1(x))
        x = self.prelu2(self.fc2(self.dropout(x)))
        x = self.prelu3(self.fc3(self.dropout(x)))
        return self.fc4(self.dropout(x))


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


def get_init_inputs():
    return [[], {'config': _mock_config(), 'input_dim': 4, 'n_labels': 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 import 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__prelu_kernel_0(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, None)
    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, None)


@triton.jit
def triton_poi_fused__prelu_kernel_1(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, None)
    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, None)


@triton.jit
def triton_poi_fused__prelu_kernel_2(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, None)
    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, None)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12
        ) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4096, 4), (4, 1))
    assert_size_stride(primals_3, (4096,), (1,))
    assert_size_stride(primals_4, (1,), (1,))
    assert_size_stride(primals_5, (2048, 4096), (4096, 1))
    assert_size_stride(primals_6, (2048,), (1,))
    assert_size_stride(primals_7, (1,), (1,))
    assert_size_stride(primals_8, (1024, 2048), (2048, 1))
    assert_size_stride(primals_9, (1024,), (1,))
    assert_size_stride(primals_10, (1,), (1,))
    assert_size_stride(primals_11, (4, 1024), (1024, 1))
    assert_size_stride(primals_12, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4096), (4096, 1), torch.float32)
        extern_kernels.addmm(primals_3, reinterpret_tensor(primals_1, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4096), (1, 4),
            0), alpha=1, beta=1, out=buf0)
        del primals_2
        del primals_3
        buf1 = empty_strided_cuda((4, 4, 4, 4096), (65536, 16384, 4096, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused__prelu_kernel_0[grid(262144)](buf0, primals_4,
            buf1, 262144, XBLOCK=1024, num_warps=4, num_stages=1)
        buf2 = empty_strided_cuda((64, 2048), (2048, 1), torch.float32)
        extern_kernels.addmm(primals_6, reinterpret_tensor(buf1, (64, 4096),
            (4096, 1), 0), reinterpret_tensor(primals_5, (4096, 2048), (1, 
            4096), 0), alpha=1, beta=1, out=buf2)
        del primals_6
        buf3 = empty_strided_cuda((4, 4, 4, 2048), (32768, 8192, 2048, 1),
            torch.float32)
        triton_poi_fused__prelu_kernel_1[grid(131072)](buf2, primals_7,
            buf3, 131072, XBLOCK=512, num_warps=8, num_stages=1)
        buf4 = empty_strided_cuda((64, 1024), (1024, 1), torch.float32)
        extern_kernels.addmm(primals_9, reinterpret_tensor(buf3, (64, 2048),
            (2048, 1), 0), reinterpret_tensor(primals_8, (2048, 1024), (1, 
            2048), 0), alpha=1, beta=1, out=buf4)
        del primals_9
        buf5 = empty_strided_cuda((4, 4, 4, 1024), (16384, 4096, 1024, 1),
            torch.float32)
        triton_poi_fused__prelu_kernel_2[grid(65536)](buf4, primals_10,
            buf5, 65536, XBLOCK=512, num_warps=4, num_stages=1)
        buf6 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_12, reinterpret_tensor(buf5, (64, 1024
            ), (1024, 1), 0), reinterpret_tensor(primals_11, (1024, 4), (1,
            1024), 0), alpha=1, beta=1, out=buf6)
        del primals_12
    return reinterpret_tensor(buf6, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), primals_4, primals_7, primals_10, reinterpret_tensor(primals_1,
        (64, 4), (4, 1), 0), buf0, reinterpret_tensor(buf1, (64, 4096), (
        4096, 1), 0), buf2, reinterpret_tensor(buf3, (64, 2048), (2048, 1), 0
        ), buf4, reinterpret_tensor(buf5, (64, 1024), (1024, 1), 0
        ), primals_11, primals_8, primals_5


class CustomClassificationHeadNew(nn.Module):

    def __init__(self, config, input_dim, n_labels):
        super().__init__()
        self.config = config
        self.fc1 = nn.Linear(input_dim, 4096)
        self.fc2 = nn.Linear(4096, 2048)
        self.fc3 = nn.Linear(2048, 1024)
        self.fc4 = nn.Linear(1024, n_labels)
        self.dropout = nn.Dropout(p=0.3)
        self.prelu1 = nn.PReLU()
        self.prelu2 = nn.PReLU()
        self.prelu3 = nn.PReLU()
        nn.init.kaiming_normal_(self.fc1.weight)
        nn.init.kaiming_normal_(self.fc2.weight)
        nn.init.kaiming_normal_(self.fc3.weight)
        nn.init.kaiming_normal_(self.fc4.weight)

    def forward(self, input_0):
        primals_2 = self.fc1.weight
        primals_3 = self.fc1.bias
        primals_5 = self.fc2.weight
        primals_6 = self.fc2.bias
        primals_8 = self.fc3.weight
        primals_9 = self.fc3.bias
        primals_11 = self.fc4.weight
        primals_12 = self.fc4.bias
        primals_4 = self.prelu1.weight
        primals_7 = self.prelu2.weight
        primals_10 = self.prelu3.weight
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12])
        return output[0]
