# 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/zj/czj4ppkgbg4fclrgfpg4n4iwm2tpdjvpwtqwdf3msd4qm6yl6tvm.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.elu]
# Source node to ATen node mapping:
#   x => expm1, gt, mul, mul_2, where
# Graph fragment:
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_1, 0), kwargs = {})
#   %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_1, 1.0), kwargs = {})
#   %expm1 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul,), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1, 1.0), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %mul, %mul_2), kwargs = {})
triton_poi_fused_elu_0 = async_compile.triton('triton_poi_fused_elu_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=[8192], 
    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_elu_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_elu_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 6848
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 1712
    x1 = (xindex // 1712)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 1.0
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.expm1(tmp4)
    tmp6 = tmp5 * tmp3
    tmp7 = tl.where(tmp2, tmp4, tmp6)
    tl.store(out_ptr0 + (x0 + (1728*x1)), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/iv/civkou2n6kqkqpmnvthoyfpsak37qzg475ip7fzmiudk3hubjfbi.py
# Topologically Sorted Source Nodes: [x, linear_1], Original ATen: [aten.elu, aten.view]
# Source node to ATen node mapping:
#   linear_1 => view_2
#   x => expm1, gt, mul, mul_2, where
# Graph fragment:
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_1, 0), kwargs = {})
#   %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_1, 1.0), kwargs = {})
#   %expm1 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul,), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1, 1.0), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %mul, %mul_2), kwargs = {})
#   %view_2 : [num_users=2] = call_function[target=torch.ops.aten.reshape.default](args = (%where, [64, 107]), kwargs = {})
triton_poi_fused_elu_view_1 = async_compile.triton('triton_poi_fused_elu_view_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=[8192], 
    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_elu_view_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_elu_view_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 6848
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 107
    x1 = (xindex // 107)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (107*(x1 % 16)) + (1728*(x1 // 16))), xmask)
    tl.store(out_ptr0 + (x2), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/5b/c5babtscwzpr6lxxnzafygax5iuya75imdizytjwpkpre4ubecha.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.elu]
# Source node to ATen node mapping:
#   x_1 => expm1_1, gt_1, mul_3, mul_5, where_1
# Graph fragment:
#   %gt_1 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_3, 0), kwargs = {})
#   %mul_3 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_3, 1.0), kwargs = {})
#   %expm1_1 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul_3,), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1_1, 1.0), kwargs = {})
#   %where_1 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %mul_3, %mul_5), kwargs = {})
triton_poi_fused_elu_2 = async_compile.triton('triton_poi_fused_elu_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=[16384], 
    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_elu_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_elu_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 11456
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 2864
    x1 = (xindex // 2864)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 1.0
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.expm1(tmp4)
    tmp6 = tmp5 * tmp3
    tmp7 = tl.where(tmp2, tmp4, tmp6)
    tl.store(out_ptr0 + (x0 + (2880*x1)), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/bh/cbhhbi6ibpsuf5kveogvylgxcbo2u2fr63t36mm7z4ka63hmof5a.py
# Topologically Sorted Source Nodes: [x_1, linear_2], Original ATen: [aten.elu, aten.view]
# Source node to ATen node mapping:
#   linear_2 => view_4
#   x_1 => expm1_1, gt_1, mul_3, mul_5, where_1
# Graph fragment:
#   %gt_1 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_3, 0), kwargs = {})
#   %mul_3 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_3, 1.0), kwargs = {})
#   %expm1_1 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul_3,), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1_1, 1.0), kwargs = {})
#   %where_1 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %mul_3, %mul_5), kwargs = {})
#   %view_4 : [num_users=2] = call_function[target=torch.ops.aten.reshape.default](args = (%where_1, [64, 179]), kwargs = {})
triton_poi_fused_elu_view_3 = async_compile.triton('triton_poi_fused_elu_view_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=[16384], 
    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_elu_view_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_elu_view_3(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 11456
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 179
    x1 = (xindex // 179)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (179*(x1 % 16)) + (2880*(x1 // 16))), xmask)
    tl.store(out_ptr0 + (x2), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/eh/cehokvi2pbt6ltr7igforlfzos6uumnasads7ro6o3jt5x7yaort.py
# Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.elu]
# Source node to ATen node mapping:
#   x_3 => expm1_3, gt_3, mul_11, mul_9, where_3
# Graph fragment:
#   %gt_3 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_7, 0), kwargs = {})
#   %mul_9 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_7, 1.0), kwargs = {})
#   %expm1_3 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul_9,), kwargs = {})
#   %mul_11 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1_3, 1.0), kwargs = {})
#   %where_3 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_3, %mul_9, %mul_11), kwargs = {})
triton_poi_fused_elu_4 = async_compile.triton('triton_poi_fused_elu_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=[16384], 
    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_elu_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_elu_4(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 11776
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 1.0
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.expm1(tmp4)
    tmp6 = tmp5 * tmp3
    tmp7 = tl.where(tmp2, tmp4, tmp6)
    tl.store(out_ptr0 + (x0), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/sn/csnz3smusu26ady3wsymfhtrdvmjbq2my7ijxowxeukxou2k5pox.py
# Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.elu]
# Source node to ATen node mapping:
#   x_4 => expm1_4, gt_4, mul_12, mul_14, where_4
# Graph fragment:
#   %gt_4 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_9, 0), kwargs = {})
#   %mul_12 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_9, 1.0), kwargs = {})
#   %expm1_4 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul_12,), kwargs = {})
#   %mul_14 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1_4, 1.0), kwargs = {})
#   %where_4 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_4, %mul_12, %mul_14), kwargs = {})
triton_poi_fused_elu_5 = async_compile.triton('triton_poi_fused_elu_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=[8192], 
    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_elu_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_elu_5(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 7360
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 1840
    x1 = (xindex // 1840)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 1.0
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.expm1(tmp4)
    tmp6 = tmp5 * tmp3
    tmp7 = tl.where(tmp2, tmp4, tmp6)
    tl.store(out_ptr0 + (x0 + (1856*x1)), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/sz/csz32npwa5zyn7szwu2yxqjrpg5rsmkv3mykg5k2ybkixcyy3cle.py
# Topologically Sorted Source Nodes: [x_4, x_5], Original ATen: [aten.elu, aten.view]
# Source node to ATen node mapping:
#   x_4 => expm1_4, gt_4, mul_12, mul_14, where_4
#   x_5 => view_10
# Graph fragment:
#   %gt_4 : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_9, 0), kwargs = {})
#   %mul_12 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_9, 1.0), kwargs = {})
#   %expm1_4 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul_12,), kwargs = {})
#   %mul_14 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1_4, 1.0), kwargs = {})
#   %where_4 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_4, %mul_12, %mul_14), kwargs = {})
#   %view_10 : [num_users=2] = call_function[target=torch.ops.aten.reshape.default](args = (%where_4, [64, 115]), kwargs = {})
triton_poi_fused_elu_view_6 = async_compile.triton('triton_poi_fused_elu_view_6', '''
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: '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_elu_view_6', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_elu_view_6(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 7360
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 115
    x1 = (xindex // 115)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (115*(x1 % 16)) + (1856*(x1 // 16))), xmask)
    tl.store(out_ptr0 + (x2), tmp0, 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 = args
    args.clear()
    assert_size_stride(primals_1, (107, 4), (4, 1))
    assert_size_stride(primals_2, (107, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (179, 107), (107, 1))
    assert_size_stride(primals_5, (179, ), (1, ))
    assert_size_stride(primals_6, (179, 179), (179, 1))
    assert_size_stride(primals_7, (179, ), (1, ))
    assert_size_stride(primals_8, (184, 179), (179, 1))
    assert_size_stride(primals_9, (184, ), (1, ))
    assert_size_stride(primals_10, (115, 184), (184, 1))
    assert_size_stride(primals_11, (115, ), (1, ))
    assert_size_stride(primals_12, (4, 115), (115, 1))
    assert_size_stride(primals_13, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 107), (107, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 107), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4, 107), (1728, 428, 107, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.elu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_elu_0.run(buf0, buf1, 6848, grid=grid(6848), stream=stream0)
        buf2 = empty_strided_cuda((64, 107), (107, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x, linear_1], Original ATen: [aten.elu, aten.view]
        triton_poi_fused_elu_view_1.run(buf1, buf2, 6848, grid=grid(6848), stream=stream0)
        del buf1
        buf3 = empty_strided_cuda((64, 179), (179, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, buf2, reinterpret_tensor(primals_4, (107, 179), (1, 107), 0), alpha=1, beta=1, out=buf3)
        del primals_5
        buf4 = empty_strided_cuda((4, 4, 4, 179), (2880, 716, 179, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.elu]
        triton_poi_fused_elu_2.run(buf3, buf4, 11456, grid=grid(11456), stream=stream0)
        buf5 = empty_strided_cuda((64, 179), (179, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_1, linear_2], Original ATen: [aten.elu, aten.view]
        triton_poi_fused_elu_view_3.run(buf4, buf5, 11456, grid=grid(11456), stream=stream0)
        buf6 = empty_strided_cuda((64, 179), (179, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_2], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_7, buf5, reinterpret_tensor(primals_6, (179, 179), (1, 179), 0), alpha=1, beta=1, out=buf6)
        del primals_7
        buf7 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.elu]
        triton_poi_fused_elu_2.run(buf6, buf7, 11456, grid=grid(11456), stream=stream0)
        buf8 = empty_strided_cuda((64, 179), (179, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_2, linear_3], Original ATen: [aten.elu, aten.view]
        triton_poi_fused_elu_view_3.run(buf7, buf8, 11456, grid=grid(11456), stream=stream0)
        del buf7
        buf9 = empty_strided_cuda((64, 184), (184, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_3], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_9, buf8, reinterpret_tensor(primals_8, (179, 184), (1, 179), 0), alpha=1, beta=1, out=buf9)
        del primals_9
        buf10 = empty_strided_cuda((4, 4, 4, 184), (2944, 736, 184, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.elu]
        triton_poi_fused_elu_4.run(buf9, buf10, 11776, grid=grid(11776), stream=stream0)
        buf11 = empty_strided_cuda((64, 115), (115, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_4], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_11, reinterpret_tensor(buf10, (64, 184), (184, 1), 0), reinterpret_tensor(primals_10, (184, 115), (1, 184), 0), alpha=1, beta=1, out=buf11)
        del primals_11
        buf12 = empty_strided_cuda((4, 4, 4, 115), (1856, 460, 115, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.elu]
        triton_poi_fused_elu_5.run(buf11, buf12, 7360, grid=grid(7360), stream=stream0)
        buf13 = empty_strided_cuda((64, 115), (115, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_4, x_5], Original ATen: [aten.elu, aten.view]
        triton_poi_fused_elu_view_6.run(buf12, buf13, 7360, grid=grid(7360), stream=stream0)
        del buf12
        buf14 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_13, buf13, reinterpret_tensor(primals_12, (115, 4), (1, 115), 0), alpha=1, beta=1, out=buf14)
        del primals_13
    return (reinterpret_tensor(buf14, (4, 4, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf0, buf2, buf3, buf5, buf6, buf8, buf9, reinterpret_tensor(buf10, (64, 184), (184, 1), 0), buf11, buf13, primals_12, primals_10, primals_8, primals_6, primals_4, )


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((107, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((107, ), (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((179, 107), (107, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((179, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((179, 179), (179, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((179, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((184, 179), (179, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((184, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((115, 184), (184, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((115, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((4, 115), (115, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = 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])
    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.optim
import torch.jit
import torch.nn as nn


class OptimizedMLP(nn.Module):

    def __init__(self, num_in_features: 'int', num_out_features: 'int'):
        super(OptimizedMLP, self).__init__()
        self.act = nn.ELU()
        self.l_in = nn.Linear(in_features=num_in_features, out_features=107)
        self.l1 = nn.Linear(in_features=107, out_features=179)
        self.l2 = nn.Linear(in_features=179, out_features=179)
        self.l3 = nn.Linear(in_features=179, out_features=184)
        self.l4 = nn.Linear(in_features=184, out_features=115)
        self.l_out = nn.Linear(in_features=115, out_features=num_out_features)
        torch.nn.init.xavier_normal_(self.l_in.weight)
        torch.nn.init.zeros_(self.l_in.bias)
        torch.nn.init.xavier_normal_(self.l1.weight)
        torch.nn.init.zeros_(self.l1.bias)
        torch.nn.init.xavier_normal_(self.l2.weight)
        torch.nn.init.zeros_(self.l2.bias)
        torch.nn.init.xavier_normal_(self.l3.weight)
        torch.nn.init.zeros_(self.l3.bias)
        torch.nn.init.xavier_normal_(self.l4.weight)
        torch.nn.init.zeros_(self.l4.bias)
        torch.nn.init.xavier_normal_(self.l_out.weight)
        torch.nn.init.zeros_(self.l_out.bias)

    def forward(self, x):
        x = self.act(self.l_in(x))
        x = self.act(self.l1(x))
        x = self.act(self.l2(x))
        x = self.act(self.l3(x))
        x = self.act(self.l4(x))
        x = self.l_out(x)
        return x


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


def get_init_inputs():
    return [[], {'num_in_features': 4, 'num_out_features': 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 torch.optim
import torch.jit
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_elu_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 6848
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 1712
    x1 = xindex // 1712
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 1.0
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.expm1(tmp4)
    tmp6 = tmp5 * tmp3
    tmp7 = tl.where(tmp2, tmp4, tmp6)
    tl.store(out_ptr0 + (x0 + 1728 * x1), tmp7, xmask)


@triton.jit
def triton_poi_fused_elu_view_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 6848
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 107
    x1 = xindex // 107
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 107 * (x1 % 16) + 1728 * (x1 // 16)), xmask)
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_elu_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 11456
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 2864
    x1 = xindex // 2864
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 1.0
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.expm1(tmp4)
    tmp6 = tmp5 * tmp3
    tmp7 = tl.where(tmp2, tmp4, tmp6)
    tl.store(out_ptr0 + (x0 + 2880 * x1), tmp7, xmask)


@triton.jit
def triton_poi_fused_elu_view_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 11456
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 179
    x1 = xindex // 179
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 179 * (x1 % 16) + 2880 * (x1 // 16)), xmask)
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_elu_4(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 11776
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 1.0
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.expm1(tmp4)
    tmp6 = tmp5 * tmp3
    tmp7 = tl.where(tmp2, tmp4, tmp6)
    tl.store(out_ptr0 + x0, tmp7, xmask)


@triton.jit
def triton_poi_fused_elu_5(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 7360
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 1840
    x1 = xindex // 1840
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 1.0
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.expm1(tmp4)
    tmp6 = tmp5 * tmp3
    tmp7 = tl.where(tmp2, tmp4, tmp6)
    tl.store(out_ptr0 + (x0 + 1856 * x1), tmp7, xmask)


@triton.jit
def triton_poi_fused_elu_view_6(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 7360
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 115
    x1 = xindex // 115
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 115 * (x1 % 16) + 1856 * (x1 // 16)), xmask)
    tl.store(out_ptr0 + x2, tmp0, 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) = args
    args.clear()
    assert_size_stride(primals_1, (107, 4), (4, 1))
    assert_size_stride(primals_2, (107,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (179, 107), (107, 1))
    assert_size_stride(primals_5, (179,), (1,))
    assert_size_stride(primals_6, (179, 179), (179, 1))
    assert_size_stride(primals_7, (179,), (1,))
    assert_size_stride(primals_8, (184, 179), (179, 1))
    assert_size_stride(primals_9, (184,), (1,))
    assert_size_stride(primals_10, (115, 184), (184, 1))
    assert_size_stride(primals_11, (115,), (1,))
    assert_size_stride(primals_12, (4, 115), (115, 1))
    assert_size_stride(primals_13, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 107), (107, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 107), (1, 4),
            0), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4, 107), (1728, 428, 107, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_elu_0[grid(6848)](buf0, buf1, 6848, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf2 = empty_strided_cuda((64, 107), (107, 1), torch.float32)
        triton_poi_fused_elu_view_1[grid(6848)](buf1, buf2, 6848, XBLOCK=
            256, num_warps=4, num_stages=1)
        del buf1
        buf3 = empty_strided_cuda((64, 179), (179, 1), torch.float32)
        extern_kernels.addmm(primals_5, buf2, reinterpret_tensor(primals_4,
            (107, 179), (1, 107), 0), alpha=1, beta=1, out=buf3)
        del primals_5
        buf4 = empty_strided_cuda((4, 4, 4, 179), (2880, 716, 179, 1),
            torch.float32)
        triton_poi_fused_elu_2[grid(11456)](buf3, buf4, 11456, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf5 = empty_strided_cuda((64, 179), (179, 1), torch.float32)
        triton_poi_fused_elu_view_3[grid(11456)](buf4, buf5, 11456, XBLOCK=
            256, num_warps=4, num_stages=1)
        buf6 = empty_strided_cuda((64, 179), (179, 1), torch.float32)
        extern_kernels.addmm(primals_7, buf5, reinterpret_tensor(primals_6,
            (179, 179), (1, 179), 0), alpha=1, beta=1, out=buf6)
        del primals_7
        buf7 = buf4
        del buf4
        triton_poi_fused_elu_2[grid(11456)](buf6, buf7, 11456, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf8 = empty_strided_cuda((64, 179), (179, 1), torch.float32)
        triton_poi_fused_elu_view_3[grid(11456)](buf7, buf8, 11456, XBLOCK=
            256, num_warps=4, num_stages=1)
        del buf7
        buf9 = empty_strided_cuda((64, 184), (184, 1), torch.float32)
        extern_kernels.addmm(primals_9, buf8, reinterpret_tensor(primals_8,
            (179, 184), (1, 179), 0), alpha=1, beta=1, out=buf9)
        del primals_9
        buf10 = empty_strided_cuda((4, 4, 4, 184), (2944, 736, 184, 1),
            torch.float32)
        triton_poi_fused_elu_4[grid(11776)](buf9, buf10, 11776, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf11 = empty_strided_cuda((64, 115), (115, 1), torch.float32)
        extern_kernels.addmm(primals_11, reinterpret_tensor(buf10, (64, 184
            ), (184, 1), 0), reinterpret_tensor(primals_10, (184, 115), (1,
            184), 0), alpha=1, beta=1, out=buf11)
        del primals_11
        buf12 = empty_strided_cuda((4, 4, 4, 115), (1856, 460, 115, 1),
            torch.float32)
        triton_poi_fused_elu_5[grid(7360)](buf11, buf12, 7360, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf13 = empty_strided_cuda((64, 115), (115, 1), torch.float32)
        triton_poi_fused_elu_view_6[grid(7360)](buf12, buf13, 7360, XBLOCK=
            256, num_warps=4, num_stages=1)
        del buf12
        buf14 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_13, buf13, reinterpret_tensor(
            primals_12, (115, 4), (1, 115), 0), alpha=1, beta=1, out=buf14)
        del primals_13
    return reinterpret_tensor(buf14, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), buf0, buf2, buf3, buf5, buf6, buf8, buf9, reinterpret_tensor(buf10,
        (64, 184), (184, 1), 0
        ), buf11, buf13, primals_12, primals_10, primals_8, primals_6, primals_4


class OptimizedMLPNew(nn.Module):

    def __init__(self, num_in_features: 'int', num_out_features: 'int'):
        super(OptimizedMLPNew, self).__init__()
        self.act = nn.ELU()
        self.l_in = nn.Linear(in_features=num_in_features, out_features=107)
        self.l1 = nn.Linear(in_features=107, out_features=179)
        self.l2 = nn.Linear(in_features=179, out_features=179)
        self.l3 = nn.Linear(in_features=179, out_features=184)
        self.l4 = nn.Linear(in_features=184, out_features=115)
        self.l_out = nn.Linear(in_features=115, out_features=num_out_features)
        torch.nn.init.xavier_normal_(self.l_in.weight)
        torch.nn.init.zeros_(self.l_in.bias)
        torch.nn.init.xavier_normal_(self.l1.weight)
        torch.nn.init.zeros_(self.l1.bias)
        torch.nn.init.xavier_normal_(self.l2.weight)
        torch.nn.init.zeros_(self.l2.bias)
        torch.nn.init.xavier_normal_(self.l3.weight)
        torch.nn.init.zeros_(self.l3.bias)
        torch.nn.init.xavier_normal_(self.l4.weight)
        torch.nn.init.zeros_(self.l4.bias)
        torch.nn.init.xavier_normal_(self.l_out.weight)
        torch.nn.init.zeros_(self.l_out.bias)

    def forward(self, input_0):
        primals_1 = self.l_in.weight
        primals_2 = self.l_in.bias
        primals_4 = self.l1.weight
        primals_5 = self.l1.bias
        primals_6 = self.l2.weight
        primals_7 = self.l2.bias
        primals_8 = self.l3.weight
        primals_9 = self.l3.bias
        primals_10 = self.l4.weight
        primals_11 = self.l4.bias
        primals_12 = self.l_out.weight
        primals_13 = self.l_out.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])
        return output[0]
