# 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/jw/cjw7ubja4hojpmayf27w3bnq2ce6edwzkqmmwo2yblsq6uncrvxt.py
# Topologically Sorted Source Nodes: [outputs], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   outputs => relu
# Graph fragment:
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {})
#   %le_7 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_relu_threshold_backward_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=[32768], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 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_relu_threshold_backward_0', '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_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 20480
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 320
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + (x2), tmp4, None)
    tl.store(out_ptr0 + (x2), tmp6, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/yg/cyg4mfxxegclwowpan5hfbi5qulziofwhyotfohv246hvg36ojqn.py
# Topologically Sorted Source Nodes: [outputs_1], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   outputs_1 => relu_1
# Graph fragment:
#   %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_3,), kwargs = {})
#   %le_6 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_1, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_1 = async_compile.triton('triton_poi_fused_relu_threshold_backward_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 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_relu_threshold_backward_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_relu_threshold_backward_1(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 10240
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 160
    tmp0 = tl.load(in_out_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + (x2), tmp4, None)
    tl.store(out_ptr0 + (x2), tmp6, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/z4/cz4tjpvurjo2r2cyp2achmcvotna3ey4qf4yvebadrzvthluvtq2.py
# Topologically Sorted Source Nodes: [outputs_2], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   outputs_2 => relu_2
# Graph fragment:
#   %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_5,), kwargs = {})
#   %le_5 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_2, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_2 = async_compile.triton('triton_poi_fused_relu_threshold_backward_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=[8192], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 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_relu_threshold_backward_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_threshold_backward_2(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 5120
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 80
    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)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + (x2), tmp4, xmask)
    tl.store(out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/sm/csmouvkzhmoguab22cjnqcceaqv67wzl3bkblgzlihakomk7mftz.py
# Topologically Sorted Source Nodes: [outputs_4], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   outputs_4 => relu_4
# Graph fragment:
#   %relu_4 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_9,), kwargs = {})
#   %le_3 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_4, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_3 = async_compile.triton('triton_poi_fused_relu_threshold_backward_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=[4096], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 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_relu_threshold_backward_3', '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_threshold_backward_3(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 2560
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 40
    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)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + (x2), tmp4, xmask)
    tl.store(out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/jd/cjdxbf6cfv7vtxvqebjbswxanlnwz7xlcjnubgimanjsio6scviu.py
# Topologically Sorted Source Nodes: [outputs_6], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   outputs_6 => relu_6
# Graph fragment:
#   %relu_6 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_13,), kwargs = {})
#   %le_1 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_6, 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=[2048], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 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_relu_threshold_backward_4', '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_threshold_backward_4(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1280
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 20
    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)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + (x2), tmp4, xmask)
    tl.store(out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/2r/c2rwtdtumv5l7idkpiu2upl3c7h56kqfajqp52padvrqleaj5qfy.py
# Topologically Sorted Source Nodes: [outputs_8], Original ATen: [aten.tanh]
# Source node to ATen node mapping:
#   outputs_8 => tanh
# Graph fragment:
#   %tanh : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%view_17,), kwargs = {})
triton_poi_fused_tanh_5 = async_compile.triton('triton_poi_fused_tanh_5', '''
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: '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_5', '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_5(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    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')


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, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19 = args
    args.clear()
    assert_size_stride(primals_1, (320, 4), (4, 1))
    assert_size_stride(primals_2, (320, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (160, 320), (320, 1))
    assert_size_stride(primals_5, (160, ), (1, ))
    assert_size_stride(primals_6, (80, 160), (160, 1))
    assert_size_stride(primals_7, (80, ), (1, ))
    assert_size_stride(primals_8, (80, 80), (80, 1))
    assert_size_stride(primals_9, (80, ), (1, ))
    assert_size_stride(primals_10, (40, 80), (80, 1))
    assert_size_stride(primals_11, (40, ), (1, ))
    assert_size_stride(primals_12, (40, 40), (40, 1))
    assert_size_stride(primals_13, (40, ), (1, ))
    assert_size_stride(primals_14, (20, 40), (40, 1))
    assert_size_stride(primals_15, (20, ), (1, ))
    assert_size_stride(primals_16, (20, 20), (20, 1))
    assert_size_stride(primals_17, (20, ), (1, ))
    assert_size_stride(primals_18, (4, 20), (20, 1))
    assert_size_stride(primals_19, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 320), (320, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 320), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 320), (5120, 1280, 320, 1), 0); del buf0  # reuse
        buf25 = empty_strided_cuda((4, 4, 4, 320), (5120, 1280, 320, 1), torch.bool)
        # Topologically Sorted Source Nodes: [outputs], Original ATen: [aten.relu, aten.threshold_backward]
        stream0 = get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0.run(buf1, primals_2, buf25, 20480, grid=grid(20480), stream=stream0)
        del primals_2
        buf2 = empty_strided_cuda((64, 160), (160, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 320), (320, 1), 0), reinterpret_tensor(primals_4, (320, 160), (1, 320), 0), out=buf2)
        buf3 = reinterpret_tensor(buf2, (4, 4, 4, 160), (2560, 640, 160, 1), 0); del buf2  # reuse
        buf24 = empty_strided_cuda((4, 4, 4, 160), (2560, 640, 160, 1), torch.bool)
        # Topologically Sorted Source Nodes: [outputs_1], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_1.run(buf3, primals_5, buf24, 10240, grid=grid(10240), stream=stream0)
        del primals_5
        buf4 = empty_strided_cuda((64, 80), (80, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf3, (64, 160), (160, 1), 0), reinterpret_tensor(primals_6, (160, 80), (1, 160), 0), out=buf4)
        buf5 = reinterpret_tensor(buf4, (4, 4, 4, 80), (1280, 320, 80, 1), 0); del buf4  # reuse
        buf23 = empty_strided_cuda((4, 4, 4, 80), (1280, 320, 80, 1), torch.bool)
        # Topologically Sorted Source Nodes: [outputs_2], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_2.run(buf5, primals_7, buf23, 5120, grid=grid(5120), stream=stream0)
        del primals_7
        buf6 = empty_strided_cuda((64, 80), (80, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf5, (64, 80), (80, 1), 0), reinterpret_tensor(primals_8, (80, 80), (1, 80), 0), out=buf6)
        buf7 = reinterpret_tensor(buf6, (4, 4, 4, 80), (1280, 320, 80, 1), 0); del buf6  # reuse
        buf22 = empty_strided_cuda((4, 4, 4, 80), (1280, 320, 80, 1), torch.bool)
        # Topologically Sorted Source Nodes: [outputs_3], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_2.run(buf7, primals_9, buf22, 5120, grid=grid(5120), stream=stream0)
        del primals_9
        buf8 = empty_strided_cuda((64, 40), (40, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf7, (64, 80), (80, 1), 0), reinterpret_tensor(primals_10, (80, 40), (1, 80), 0), out=buf8)
        buf9 = reinterpret_tensor(buf8, (4, 4, 4, 40), (640, 160, 40, 1), 0); del buf8  # reuse
        buf21 = empty_strided_cuda((4, 4, 4, 40), (640, 160, 40, 1), torch.bool)
        # Topologically Sorted Source Nodes: [outputs_4], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_3.run(buf9, primals_11, buf21, 2560, grid=grid(2560), stream=stream0)
        del primals_11
        buf10 = empty_strided_cuda((64, 40), (40, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf9, (64, 40), (40, 1), 0), reinterpret_tensor(primals_12, (40, 40), (1, 40), 0), out=buf10)
        buf11 = reinterpret_tensor(buf10, (4, 4, 4, 40), (640, 160, 40, 1), 0); del buf10  # reuse
        buf20 = empty_strided_cuda((4, 4, 4, 40), (640, 160, 40, 1), torch.bool)
        # Topologically Sorted Source Nodes: [outputs_5], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_3.run(buf11, primals_13, buf20, 2560, grid=grid(2560), stream=stream0)
        del primals_13
        buf12 = empty_strided_cuda((64, 20), (20, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf11, (64, 40), (40, 1), 0), reinterpret_tensor(primals_14, (40, 20), (1, 40), 0), out=buf12)
        buf13 = reinterpret_tensor(buf12, (4, 4, 4, 20), (320, 80, 20, 1), 0); del buf12  # reuse
        buf19 = empty_strided_cuda((4, 4, 4, 20), (320, 80, 20, 1), torch.bool)
        # Topologically Sorted Source Nodes: [outputs_6], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_4.run(buf13, primals_15, buf19, 1280, grid=grid(1280), stream=stream0)
        del primals_15
        buf14 = empty_strided_cuda((64, 20), (20, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf13, (64, 20), (20, 1), 0), reinterpret_tensor(primals_16, (20, 20), (1, 20), 0), out=buf14)
        buf15 = reinterpret_tensor(buf14, (4, 4, 4, 20), (320, 80, 20, 1), 0); del buf14  # reuse
        buf18 = empty_strided_cuda((4, 4, 4, 20), (320, 80, 20, 1), torch.bool)
        # Topologically Sorted Source Nodes: [outputs_7], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_4.run(buf15, primals_17, buf18, 1280, grid=grid(1280), stream=stream0)
        del primals_17
        buf16 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf15, (64, 20), (20, 1), 0), reinterpret_tensor(primals_18, (20, 4), (1, 20), 0), out=buf16)
        buf17 = reinterpret_tensor(buf16, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf16  # reuse
        # Topologically Sorted Source Nodes: [outputs_8], Original ATen: [aten.tanh]
        triton_poi_fused_tanh_5.run(buf17, primals_19, 256, grid=grid(256), stream=stream0)
        del primals_19
    return (buf17, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(buf1, (64, 320), (320, 1), 0), reinterpret_tensor(buf3, (64, 160), (160, 1), 0), reinterpret_tensor(buf5, (64, 80), (80, 1), 0), reinterpret_tensor(buf7, (64, 80), (80, 1), 0), reinterpret_tensor(buf9, (64, 40), (40, 1), 0), reinterpret_tensor(buf11, (64, 40), (40, 1), 0), reinterpret_tensor(buf13, (64, 20), (20, 1), 0), reinterpret_tensor(buf15, (64, 20), (20, 1), 0), buf17, primals_18, buf18, primals_16, buf19, primals_14, buf20, primals_12, buf21, primals_10, buf22, primals_8, buf23, primals_6, buf24, primals_4, buf25, )


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((320, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((320, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((160, 320), (320, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((160, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((80, 160), (160, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((80, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((80, 80), (80, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((80, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((40, 80), (80, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((40, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((40, 40), (40, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((40, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((20, 40), (40, 1), device='cuda:0', dtype=torch.float32)
    primals_15 = rand_strided((20, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_16 = rand_strided((20, 20), (20, 1), device='cuda:0', dtype=torch.float32)
    primals_17 = rand_strided((20, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_18 = rand_strided((4, 20), (20, 1), device='cuda:0', dtype=torch.float32)
    primals_19 = 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, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19])
    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


class AlphaSlow(nn.Module):

    def __init__(self, n_in, n_out):
        super(AlphaSlow, self).__init__()
        self.fc1 = nn.Linear(n_in, 320, bias=True)
        self.fc2 = nn.Linear(320, 160, bias=True)
        self.fc3 = nn.Linear(160, 80, bias=True)
        self.fc4 = nn.Linear(80, 80, bias=True)
        self.fc5 = nn.Linear(80, 40, bias=True)
        self.fc6 = nn.Linear(40, 40, bias=True)
        self.fc7 = nn.Linear(40, 20, bias=True)
        self.fc8 = nn.Linear(20, 20, bias=True)
        self.fc9 = nn.Linear(20, n_out, bias=True)
        self.sigmoid = nn.Sigmoid()
        self.Tanh = nn.Tanh()
        self.ReLU = nn.ReLU()

    def forward(self, inputs):
        outputs = self.ReLU(self.fc1(inputs))
        outputs = self.ReLU(self.fc2(outputs))
        outputs = self.ReLU(self.fc3(outputs))
        outputs = self.ReLU(self.fc4(outputs))
        outputs = self.ReLU(self.fc5(outputs))
        outputs = self.ReLU(self.fc6(outputs))
        outputs = self.ReLU(self.fc7(outputs))
        outputs = self.ReLU(self.fc8(outputs))
        outputs = self.Tanh(self.fc9(outputs))
        return outputs


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


def get_init_inputs():
    return [[], {'n_in': 4, 'n_out': 4}]

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
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_relu_threshold_backward_0(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 320
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, None)
    tl.store(out_ptr0 + x2, tmp6, None)


@triton.jit
def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 160
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, None)
    tl.store(out_ptr0 + x2, tmp6, None)


@triton.jit
def triton_poi_fused_relu_threshold_backward_2(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 5120
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 80
    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)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_3(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 2560
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 40
    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)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_4(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1280
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 20
    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)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_tanh_5(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    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)


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,
        primals_13, primals_14, primals_15, primals_16, primals_17,
        primals_18, primals_19) = args
    args.clear()
    assert_size_stride(primals_1, (320, 4), (4, 1))
    assert_size_stride(primals_2, (320,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (160, 320), (320, 1))
    assert_size_stride(primals_5, (160,), (1,))
    assert_size_stride(primals_6, (80, 160), (160, 1))
    assert_size_stride(primals_7, (80,), (1,))
    assert_size_stride(primals_8, (80, 80), (80, 1))
    assert_size_stride(primals_9, (80,), (1,))
    assert_size_stride(primals_10, (40, 80), (80, 1))
    assert_size_stride(primals_11, (40,), (1,))
    assert_size_stride(primals_12, (40, 40), (40, 1))
    assert_size_stride(primals_13, (40,), (1,))
    assert_size_stride(primals_14, (20, 40), (40, 1))
    assert_size_stride(primals_15, (20,), (1,))
    assert_size_stride(primals_16, (20, 20), (20, 1))
    assert_size_stride(primals_17, (20,), (1,))
    assert_size_stride(primals_18, (4, 20), (20, 1))
    assert_size_stride(primals_19, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 320), (320, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 320), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 320), (5120, 1280, 320, 1), 0
            )
        del buf0
        buf25 = empty_strided_cuda((4, 4, 4, 320), (5120, 1280, 320, 1),
            torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(20480)](buf1,
            primals_2, buf25, 20480, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((64, 160), (160, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 320), (320, 1), 0),
            reinterpret_tensor(primals_4, (320, 160), (1, 320), 0), out=buf2)
        buf3 = reinterpret_tensor(buf2, (4, 4, 4, 160), (2560, 640, 160, 1), 0)
        del buf2
        buf24 = empty_strided_cuda((4, 4, 4, 160), (2560, 640, 160, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_1[grid(10240)](buf3,
            primals_5, buf24, 10240, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((64, 80), (80, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf3, (64, 160), (160, 1), 0),
            reinterpret_tensor(primals_6, (160, 80), (1, 160), 0), out=buf4)
        buf5 = reinterpret_tensor(buf4, (4, 4, 4, 80), (1280, 320, 80, 1), 0)
        del buf4
        buf23 = empty_strided_cuda((4, 4, 4, 80), (1280, 320, 80, 1), torch
            .bool)
        triton_poi_fused_relu_threshold_backward_2[grid(5120)](buf5,
            primals_7, buf23, 5120, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_7
        buf6 = empty_strided_cuda((64, 80), (80, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf5, (64, 80), (80, 1), 0),
            reinterpret_tensor(primals_8, (80, 80), (1, 80), 0), out=buf6)
        buf7 = reinterpret_tensor(buf6, (4, 4, 4, 80), (1280, 320, 80, 1), 0)
        del buf6
        buf22 = empty_strided_cuda((4, 4, 4, 80), (1280, 320, 80, 1), torch
            .bool)
        triton_poi_fused_relu_threshold_backward_2[grid(5120)](buf7,
            primals_9, buf22, 5120, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_9
        buf8 = empty_strided_cuda((64, 40), (40, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf7, (64, 80), (80, 1), 0),
            reinterpret_tensor(primals_10, (80, 40), (1, 80), 0), out=buf8)
        buf9 = reinterpret_tensor(buf8, (4, 4, 4, 40), (640, 160, 40, 1), 0)
        del buf8
        buf21 = empty_strided_cuda((4, 4, 4, 40), (640, 160, 40, 1), torch.bool
            )
        triton_poi_fused_relu_threshold_backward_3[grid(2560)](buf9,
            primals_11, buf21, 2560, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_11
        buf10 = empty_strided_cuda((64, 40), (40, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf9, (64, 40), (40, 1), 0),
            reinterpret_tensor(primals_12, (40, 40), (1, 40), 0), out=buf10)
        buf11 = reinterpret_tensor(buf10, (4, 4, 4, 40), (640, 160, 40, 1), 0)
        del buf10
        buf20 = empty_strided_cuda((4, 4, 4, 40), (640, 160, 40, 1), torch.bool
            )
        triton_poi_fused_relu_threshold_backward_3[grid(2560)](buf11,
            primals_13, buf20, 2560, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_13
        buf12 = empty_strided_cuda((64, 20), (20, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf11, (64, 40), (40, 1), 0),
            reinterpret_tensor(primals_14, (40, 20), (1, 40), 0), out=buf12)
        buf13 = reinterpret_tensor(buf12, (4, 4, 4, 20), (320, 80, 20, 1), 0)
        del buf12
        buf19 = empty_strided_cuda((4, 4, 4, 20), (320, 80, 20, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_4[grid(1280)](buf13,
            primals_15, buf19, 1280, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_15
        buf14 = empty_strided_cuda((64, 20), (20, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf13, (64, 20), (20, 1), 0),
            reinterpret_tensor(primals_16, (20, 20), (1, 20), 0), out=buf14)
        buf15 = reinterpret_tensor(buf14, (4, 4, 4, 20), (320, 80, 20, 1), 0)
        del buf14
        buf18 = empty_strided_cuda((4, 4, 4, 20), (320, 80, 20, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_4[grid(1280)](buf15,
            primals_17, buf18, 1280, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_17
        buf16 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf15, (64, 20), (20, 1), 0),
            reinterpret_tensor(primals_18, (20, 4), (1, 20), 0), out=buf16)
        buf17 = reinterpret_tensor(buf16, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf16
        triton_poi_fused_tanh_5[grid(256)](buf17, primals_19, 256, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_19
    return (buf17, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
        reinterpret_tensor(buf1, (64, 320), (320, 1), 0),
        reinterpret_tensor(buf3, (64, 160), (160, 1), 0),
        reinterpret_tensor(buf5, (64, 80), (80, 1), 0), reinterpret_tensor(
        buf7, (64, 80), (80, 1), 0), reinterpret_tensor(buf9, (64, 40), (40,
        1), 0), reinterpret_tensor(buf11, (64, 40), (40, 1), 0),
        reinterpret_tensor(buf13, (64, 20), (20, 1), 0), reinterpret_tensor
        (buf15, (64, 20), (20, 1), 0), buf17, primals_18, buf18, primals_16,
        buf19, primals_14, buf20, primals_12, buf21, primals_10, buf22,
        primals_8, buf23, primals_6, buf24, primals_4, buf25)


class AlphaSlowNew(nn.Module):

    def __init__(self, n_in, n_out):
        super(AlphaSlowNew, self).__init__()
        self.fc1 = nn.Linear(n_in, 320, bias=True)
        self.fc2 = nn.Linear(320, 160, bias=True)
        self.fc3 = nn.Linear(160, 80, bias=True)
        self.fc4 = nn.Linear(80, 80, bias=True)
        self.fc5 = nn.Linear(80, 40, bias=True)
        self.fc6 = nn.Linear(40, 40, bias=True)
        self.fc7 = nn.Linear(40, 20, bias=True)
        self.fc8 = nn.Linear(20, 20, bias=True)
        self.fc9 = nn.Linear(20, n_out, bias=True)
        self.sigmoid = nn.Sigmoid()
        self.Tanh = nn.Tanh()
        self.ReLU = nn.ReLU()

    def forward(self, input_0):
        primals_1 = self.fc1.weight
        primals_2 = self.fc1.bias
        primals_4 = self.fc2.weight
        primals_5 = self.fc2.bias
        primals_6 = self.fc3.weight
        primals_7 = self.fc3.bias
        primals_8 = self.fc4.weight
        primals_9 = self.fc4.bias
        primals_10 = self.fc5.weight
        primals_11 = self.fc5.bias
        primals_12 = self.fc6.weight
        primals_13 = self.fc6.bias
        primals_14 = self.fc7.weight
        primals_15 = self.fc7.bias
        primals_16 = self.fc8.weight
        primals_17 = self.fc8.bias
        primals_18 = self.fc9.weight
        primals_19 = self.fc9.bias
        primals_3 = 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, primals_13, primals_14,
            primals_15, primals_16, primals_17, primals_18, primals_19])
        return output[0]
