# 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/cw/ccwssfx6qcvunhe3oavlbicw6nznahssfugkgjkuhgpjkulcytk6.py
# Topologically Sorted Source Nodes: [out_1], Original ATen: [aten.leaky_relu]
# Source node to ATen node mapping:
#   out_1 => gt, mul, where
# Graph fragment:
#   %gt : [num_users=2] = 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_1, 0.01), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %view_1, %mul), kwargs = {})
triton_poi_fused_leaky_relu_0 = async_compile.triton('triton_poi_fused_leaky_relu_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=[1024], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_leaky_relu_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_leaky_relu_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + (x2), tmp4, xmask)
    tl.store(out_ptr1 + (x2), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/mb/cmbsdmqnevipwmbqgqx2bf34tpzu2mclezyvh2hjavdeq4ms7h6y.py
# Topologically Sorted Source Nodes: [out_3], Original ATen: [aten.leaky_relu]
# Source node to ATen node mapping:
#   out_3 => gt_1, mul_1, where_1
# Graph fragment:
#   %gt_1 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_3, 0), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_3, 0.01), kwargs = {})
#   %where_1 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_1, %view_3, %mul_1), kwargs = {})
triton_poi_fused_leaky_relu_1 = async_compile.triton('triton_poi_fused_leaky_relu_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=[2048], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_leaky_relu_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_leaky_relu_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 2048
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 32
    tmp0 = tl.load(in_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + (x2), tmp4, None)
    tl.store(out_ptr1 + (x2), tmp7, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/64/c64e7fv6lemz2halxnpuvaeiuojnzoec3h32jk4stustlcnfwuwm.py
# Topologically Sorted Source Nodes: [out_5], Original ATen: [aten.leaky_relu]
# Source node to ATen node mapping:
#   out_5 => gt_2, mul_2, where_2
# Graph fragment:
#   %gt_2 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_5, 0), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_5, 0.01), kwargs = {})
#   %where_2 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_2, %view_5, %mul_2), kwargs = {})
triton_poi_fused_leaky_relu_2 = async_compile.triton('triton_poi_fused_leaky_relu_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=[4096], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_leaky_relu_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_leaky_relu_2(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + (x2), tmp4, None)
    tl.store(out_ptr1 + (x2), tmp7, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/xc/cxcrfkbyzif3efshywa3lhxtvgyh2s3suanqgnyt3a7i7tb3gujn.py
# Topologically Sorted Source Nodes: [out_7], Original ATen: [aten.leaky_relu]
# Source node to ATen node mapping:
#   out_7 => gt_3, mul_3, where_3
# Graph fragment:
#   %gt_3 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_7, 0), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_7, 0.01), kwargs = {})
#   %where_3 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_3, %view_7, %mul_3), kwargs = {})
triton_poi_fused_leaky_relu_3 = async_compile.triton('triton_poi_fused_leaky_relu_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=[8192], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_leaky_relu_3', '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_leaky_relu_3(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + (x2), tmp4, None)
    tl.store(out_ptr1 + (x2), tmp7, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/6b/c6bzzj7jhfh6jyhe5cqdrid6jfurueetai5mq7x2oanvqbw2b4ea.py
# Topologically Sorted Source Nodes: [out_9], Original ATen: [aten.leaky_relu]
# Source node to ATen node mapping:
#   out_9 => gt_4, mul_4, where_4
# Graph fragment:
#   %gt_4 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_9, 0), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_9, 0.01), kwargs = {})
#   %where_4 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_4, %view_9, %mul_4), kwargs = {})
triton_poi_fused_leaky_relu_4 = async_compile.triton('triton_poi_fused_leaky_relu_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: '*i1', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_leaky_relu_4', '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_leaky_relu_4(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 256
    tmp0 = tl.load(in_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + (x2), tmp4, None)
    tl.store(out_ptr1 + (x2), tmp7, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/q5/cq52367iy7qxerabkae5oft5s5ysbjmasgh4nqtrcegjfkrvztq2.py
# Topologically Sorted Source Nodes: [out_11], Original ATen: [aten.leaky_relu]
# Source node to ATen node mapping:
#   out_11 => gt_5, mul_5, where_5
# Graph fragment:
#   %gt_5 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_11, 0), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_11, 0.01), kwargs = {})
#   %where_5 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_5, %view_11, %mul_5), kwargs = {})
triton_poi_fused_leaky_relu_5 = async_compile.triton('triton_poi_fused_leaky_relu_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=[32768], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_leaky_relu_5', '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_leaky_relu_5(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 512
    tmp0 = tl.load(in_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + (x2), tmp4, None)
    tl.store(out_ptr1 + (x2), tmp7, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/ha/chatowqegrdo5jmrxbgmwc5lv7vfx6pis2pjupqtenu4dcrurqz6.py
# Topologically Sorted Source Nodes: [out_27], Original ATen: [aten.leaky_relu]
# Source node to ATen node mapping:
#   out_27 => gt_13, mul_13, where_13
# Graph fragment:
#   %gt_13 : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_27, 0), kwargs = {})
#   %mul_13 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_27, 0.01), kwargs = {})
#   %where_13 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt_13, %view_27, %mul_13), kwargs = {})
triton_poi_fused_leaky_relu_6 = async_compile.triton('triton_poi_fused_leaky_relu_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_leaky_relu_6', '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_leaky_relu_6(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 3
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + (x2), tmp4, xmask)
    tl.store(out_ptr1 + (x2), tmp7, 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, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27, primals_28, primals_29 = args
    args.clear()
    assert_size_stride(primals_1, (16, 4), (4, 1))
    assert_size_stride(primals_2, (16, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (32, 16), (16, 1))
    assert_size_stride(primals_5, (32, ), (1, ))
    assert_size_stride(primals_6, (64, 32), (32, 1))
    assert_size_stride(primals_7, (64, ), (1, ))
    assert_size_stride(primals_8, (128, 64), (64, 1))
    assert_size_stride(primals_9, (128, ), (1, ))
    assert_size_stride(primals_10, (256, 128), (128, 1))
    assert_size_stride(primals_11, (256, ), (1, ))
    assert_size_stride(primals_12, (512, 256), (256, 1))
    assert_size_stride(primals_13, (512, ), (1, ))
    assert_size_stride(primals_14, (512, 512), (512, 1))
    assert_size_stride(primals_15, (512, ), (1, ))
    assert_size_stride(primals_16, (512, 512), (512, 1))
    assert_size_stride(primals_17, (512, ), (1, ))
    assert_size_stride(primals_18, (512, 512), (512, 1))
    assert_size_stride(primals_19, (512, ), (1, ))
    assert_size_stride(primals_20, (256, 512), (512, 1))
    assert_size_stride(primals_21, (256, ), (1, ))
    assert_size_stride(primals_22, (128, 256), (256, 1))
    assert_size_stride(primals_23, (128, ), (1, ))
    assert_size_stride(primals_24, (64, 128), (128, 1))
    assert_size_stride(primals_25, (64, ), (1, ))
    assert_size_stride(primals_26, (32, 64), (64, 1))
    assert_size_stride(primals_27, (32, ), (1, ))
    assert_size_stride(primals_28, (3, 32), (32, 1))
    assert_size_stride(primals_29, (3, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 16), (16, 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, 16), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4, 16), (256, 64, 16, 1), torch.bool)
        buf2 = empty_strided_cuda((4, 4, 4, 16), (256, 64, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_1], Original ATen: [aten.leaky_relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_leaky_relu_0.run(buf0, primals_2, buf1, buf2, 1024, grid=grid(1024), stream=stream0)
        del buf0
        del primals_2
        buf3 = empty_strided_cuda((64, 32), (32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf2, (64, 16), (16, 1), 0), reinterpret_tensor(primals_4, (16, 32), (1, 16), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 4, 4, 32), (512, 128, 32, 1), torch.bool)
        buf5 = empty_strided_cuda((4, 4, 4, 32), (512, 128, 32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_3], Original ATen: [aten.leaky_relu]
        triton_poi_fused_leaky_relu_1.run(buf3, primals_5, buf4, buf5, 2048, grid=grid(2048), stream=stream0)
        del primals_5
        buf6 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf5, (64, 32), (32, 1), 0), reinterpret_tensor(primals_6, (32, 64), (1, 32), 0), out=buf6)
        buf7 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.bool)
        buf8 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_5], Original ATen: [aten.leaky_relu]
        triton_poi_fused_leaky_relu_2.run(buf6, primals_7, buf7, buf8, 4096, grid=grid(4096), stream=stream0)
        del primals_7
        buf9 = empty_strided_cuda((64, 128), (128, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf8, (64, 64), (64, 1), 0), reinterpret_tensor(primals_8, (64, 128), (1, 64), 0), out=buf9)
        buf10 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1), torch.bool)
        buf11 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_7], Original ATen: [aten.leaky_relu]
        triton_poi_fused_leaky_relu_3.run(buf9, primals_9, buf10, buf11, 8192, grid=grid(8192), stream=stream0)
        del primals_9
        buf12 = empty_strided_cuda((64, 256), (256, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf11, (64, 128), (128, 1), 0), reinterpret_tensor(primals_10, (128, 256), (1, 128), 0), out=buf12)
        buf13 = empty_strided_cuda((4, 4, 4, 256), (4096, 1024, 256, 1), torch.bool)
        buf14 = empty_strided_cuda((4, 4, 4, 256), (4096, 1024, 256, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_9], Original ATen: [aten.leaky_relu]
        triton_poi_fused_leaky_relu_4.run(buf12, primals_11, buf13, buf14, 16384, grid=grid(16384), stream=stream0)
        del primals_11
        buf15 = empty_strided_cuda((64, 512), (512, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf14, (64, 256), (256, 1), 0), reinterpret_tensor(primals_12, (256, 512), (1, 256), 0), out=buf15)
        buf16 = empty_strided_cuda((4, 4, 4, 512), (8192, 2048, 512, 1), torch.bool)
        buf17 = empty_strided_cuda((4, 4, 4, 512), (8192, 2048, 512, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_11], Original ATen: [aten.leaky_relu]
        triton_poi_fused_leaky_relu_5.run(buf15, primals_13, buf16, buf17, 32768, grid=grid(32768), stream=stream0)
        del primals_13
        buf18 = buf15; del buf15  # reuse
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf17, (64, 512), (512, 1), 0), reinterpret_tensor(primals_14, (512, 512), (1, 512), 0), out=buf18)
        buf19 = empty_strided_cuda((4, 4, 4, 512), (8192, 2048, 512, 1), torch.bool)
        buf20 = empty_strided_cuda((4, 4, 4, 512), (8192, 2048, 512, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_13], Original ATen: [aten.leaky_relu]
        triton_poi_fused_leaky_relu_5.run(buf18, primals_15, buf19, buf20, 32768, grid=grid(32768), stream=stream0)
        del primals_15
        buf21 = buf18; del buf18  # reuse
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf20, (64, 512), (512, 1), 0), reinterpret_tensor(primals_16, (512, 512), (1, 512), 0), out=buf21)
        buf22 = empty_strided_cuda((4, 4, 4, 512), (8192, 2048, 512, 1), torch.bool)
        buf23 = empty_strided_cuda((4, 4, 4, 512), (8192, 2048, 512, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_15], Original ATen: [aten.leaky_relu]
        triton_poi_fused_leaky_relu_5.run(buf21, primals_17, buf22, buf23, 32768, grid=grid(32768), stream=stream0)
        del primals_17
        buf24 = buf21; del buf21  # reuse
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf23, (64, 512), (512, 1), 0), reinterpret_tensor(primals_18, (512, 512), (1, 512), 0), out=buf24)
        buf25 = empty_strided_cuda((4, 4, 4, 512), (8192, 2048, 512, 1), torch.bool)
        buf26 = empty_strided_cuda((4, 4, 4, 512), (8192, 2048, 512, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_17], Original ATen: [aten.leaky_relu]
        triton_poi_fused_leaky_relu_5.run(buf24, primals_19, buf25, buf26, 32768, grid=grid(32768), stream=stream0)
        del buf24
        del primals_19
        buf27 = buf12; del buf12  # reuse
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf26, (64, 512), (512, 1), 0), reinterpret_tensor(primals_20, (512, 256), (1, 512), 0), out=buf27)
        buf28 = empty_strided_cuda((4, 4, 4, 256), (4096, 1024, 256, 1), torch.bool)
        buf29 = empty_strided_cuda((4, 4, 4, 256), (4096, 1024, 256, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_19], Original ATen: [aten.leaky_relu]
        triton_poi_fused_leaky_relu_4.run(buf27, primals_21, buf28, buf29, 16384, grid=grid(16384), stream=stream0)
        del buf27
        del primals_21
        buf30 = buf9; del buf9  # reuse
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf29, (64, 256), (256, 1), 0), reinterpret_tensor(primals_22, (256, 128), (1, 256), 0), out=buf30)
        buf31 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1), torch.bool)
        buf32 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_21], Original ATen: [aten.leaky_relu]
        triton_poi_fused_leaky_relu_3.run(buf30, primals_23, buf31, buf32, 8192, grid=grid(8192), stream=stream0)
        del buf30
        del primals_23
        buf33 = buf6; del buf6  # reuse
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf32, (64, 128), (128, 1), 0), reinterpret_tensor(primals_24, (128, 64), (1, 128), 0), out=buf33)
        buf34 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.bool)
        buf35 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_23], Original ATen: [aten.leaky_relu]
        triton_poi_fused_leaky_relu_2.run(buf33, primals_25, buf34, buf35, 4096, grid=grid(4096), stream=stream0)
        del buf33
        del primals_25
        buf36 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf35, (64, 64), (64, 1), 0), reinterpret_tensor(primals_26, (64, 32), (1, 64), 0), out=buf36)
        buf37 = empty_strided_cuda((4, 4, 4, 32), (512, 128, 32, 1), torch.bool)
        buf38 = empty_strided_cuda((4, 4, 4, 32), (512, 128, 32, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_25], Original ATen: [aten.leaky_relu]
        triton_poi_fused_leaky_relu_1.run(buf36, primals_27, buf37, buf38, 2048, grid=grid(2048), stream=stream0)
        del buf36
        del primals_27
        buf39 = empty_strided_cuda((64, 3), (3, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf38, (64, 32), (32, 1), 0), reinterpret_tensor(primals_28, (32, 3), (1, 32), 0), out=buf39)
        buf40 = empty_strided_cuda((4, 4, 4, 3), (48, 12, 3, 1), torch.bool)
        buf41 = empty_strided_cuda((4, 4, 4, 3), (48, 12, 3, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_27], Original ATen: [aten.leaky_relu]
        triton_poi_fused_leaky_relu_6.run(buf39, primals_29, buf40, buf41, 192, grid=grid(192), stream=stream0)
        del buf39
        del primals_29
    return (buf41, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf1, reinterpret_tensor(buf2, (64, 16), (16, 1), 0), buf4, reinterpret_tensor(buf5, (64, 32), (32, 1), 0), buf7, reinterpret_tensor(buf8, (64, 64), (64, 1), 0), buf10, reinterpret_tensor(buf11, (64, 128), (128, 1), 0), buf13, reinterpret_tensor(buf14, (64, 256), (256, 1), 0), buf16, reinterpret_tensor(buf17, (64, 512), (512, 1), 0), buf19, reinterpret_tensor(buf20, (64, 512), (512, 1), 0), buf22, reinterpret_tensor(buf23, (64, 512), (512, 1), 0), buf25, reinterpret_tensor(buf26, (64, 512), (512, 1), 0), buf28, reinterpret_tensor(buf29, (64, 256), (256, 1), 0), buf31, reinterpret_tensor(buf32, (64, 128), (128, 1), 0), buf34, reinterpret_tensor(buf35, (64, 64), (64, 1), 0), buf37, reinterpret_tensor(buf38, (64, 32), (32, 1), 0), buf40, primals_28, primals_26, primals_24, primals_22, primals_20, primals_18, primals_16, primals_14, 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((16, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((16, ), (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((32, 16), (16, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((64, 32), (32, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((128, 64), (64, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((256, 128), (128, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((512, 256), (256, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((512, 512), (512, 1), device='cuda:0', dtype=torch.float32)
    primals_15 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_16 = rand_strided((512, 512), (512, 1), device='cuda:0', dtype=torch.float32)
    primals_17 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_18 = rand_strided((512, 512), (512, 1), device='cuda:0', dtype=torch.float32)
    primals_19 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_20 = rand_strided((256, 512), (512, 1), device='cuda:0', dtype=torch.float32)
    primals_21 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_22 = rand_strided((128, 256), (256, 1), device='cuda:0', dtype=torch.float32)
    primals_23 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_24 = rand_strided((64, 128), (128, 1), device='cuda:0', dtype=torch.float32)
    primals_25 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_26 = rand_strided((32, 64), (64, 1), device='cuda:0', dtype=torch.float32)
    primals_27 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_28 = rand_strided((3, 32), (32, 1), device='cuda:0', dtype=torch.float32)
    primals_29 = rand_strided((3, ), (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, primals_20, primals_21, primals_22, primals_23, primals_24, primals_25, primals_26, primals_27, primals_28, primals_29])
    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 Model(nn.Module):

    def __init__(self, inputdim):
        super(Model, self).__init__()
        self.layer1 = nn.Linear(inputdim, 16)
        torch.nn.init.xavier_uniform_(self.layer1.weight)
        self.layer2 = nn.Linear(16, 32)
        torch.nn.init.xavier_uniform_(self.layer2.weight)
        self.layer3 = nn.Linear(32, 64)
        torch.nn.init.xavier_uniform_(self.layer3.weight)
        self.layer4 = nn.Linear(64, 128)
        torch.nn.init.xavier_uniform_(self.layer4.weight)
        self.layer5 = nn.Linear(128, 256)
        torch.nn.init.xavier_uniform_(self.layer5.weight)
        self.layer6 = nn.Linear(256, 512)
        torch.nn.init.xavier_uniform_(self.layer6.weight)
        self.layer11 = nn.Linear(512, 512)
        torch.nn.init.xavier_uniform_(self.layer11.weight)
        self.layer12 = nn.Linear(512, 512)
        torch.nn.init.xavier_uniform_(self.layer12.weight)
        self.layer13 = nn.Linear(512, 512)
        torch.nn.init.xavier_uniform_(self.layer13.weight)
        self.layer14 = nn.Linear(512, 256)
        torch.nn.init.xavier_uniform_(self.layer14.weight)
        self.layer7 = nn.Linear(256, 128)
        torch.nn.init.xavier_uniform_(self.layer7.weight)
        self.layer8 = nn.Linear(128, 64)
        torch.nn.init.xavier_uniform_(self.layer8.weight)
        self.layer9 = nn.Linear(64, 32)
        torch.nn.init.xavier_uniform_(self.layer9.weight)
        self.layer10 = nn.Linear(32, 3)
        torch.nn.init.xavier_uniform_(self.layer10.weight)

    def forward(self, motor_control):
        out = self.layer1(motor_control)
        out = nn.LeakyReLU()(out)
        out = self.layer2(out)
        out = nn.LeakyReLU()(out)
        out = self.layer3(out)
        out = nn.LeakyReLU()(out)
        out = self.layer4(out)
        out = nn.LeakyReLU()(out)
        out = self.layer5(out)
        out = nn.LeakyReLU()(out)
        out = self.layer6(out)
        out = nn.LeakyReLU()(out)
        out = self.layer11(out)
        out = nn.LeakyReLU()(out)
        out = self.layer12(out)
        out = nn.LeakyReLU()(out)
        out = self.layer13(out)
        out = nn.LeakyReLU()(out)
        out = self.layer14(out)
        out = nn.LeakyReLU()(out)
        out = self.layer7(out)
        out = nn.LeakyReLU()(out)
        out = self.layer8(out)
        out = nn.LeakyReLU()(out)
        out = self.layer9(out)
        out = nn.LeakyReLU()(out)
        out = self.layer10(out)
        out = nn.LeakyReLU()(out)
        pos_value = out
        return pos_value


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


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

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_leaky_relu_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr1 + x2, tmp7, xmask)


@triton.jit
def triton_poi_fused_leaky_relu_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    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 % 32
    tmp0 = tl.load(in_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x2, tmp4, None)
    tl.store(out_ptr1 + x2, tmp7, None)


@triton.jit
def triton_poi_fused_leaky_relu_2(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    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 % 64
    tmp0 = tl.load(in_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x2, tmp4, None)
    tl.store(out_ptr1 + x2, tmp7, None)


@triton.jit
def triton_poi_fused_leaky_relu_3(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    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 % 128
    tmp0 = tl.load(in_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x2, tmp4, None)
    tl.store(out_ptr1 + x2, tmp7, None)


@triton.jit
def triton_poi_fused_leaky_relu_4(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    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 % 256
    tmp0 = tl.load(in_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x2, tmp4, None)
    tl.store(out_ptr1 + x2, tmp7, None)


@triton.jit
def triton_poi_fused_leaky_relu_5(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    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 % 512
    tmp0 = tl.load(in_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x2, tmp4, None)
    tl.store(out_ptr1 + x2, tmp7, None)


@triton.jit
def triton_poi_fused_leaky_relu_6(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 3
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr1 + x2, tmp7, 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, primals_20, primals_21, primals_22,
        primals_23, primals_24, primals_25, primals_26, primals_27,
        primals_28, primals_29) = args
    args.clear()
    assert_size_stride(primals_1, (16, 4), (4, 1))
    assert_size_stride(primals_2, (16,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (32, 16), (16, 1))
    assert_size_stride(primals_5, (32,), (1,))
    assert_size_stride(primals_6, (64, 32), (32, 1))
    assert_size_stride(primals_7, (64,), (1,))
    assert_size_stride(primals_8, (128, 64), (64, 1))
    assert_size_stride(primals_9, (128,), (1,))
    assert_size_stride(primals_10, (256, 128), (128, 1))
    assert_size_stride(primals_11, (256,), (1,))
    assert_size_stride(primals_12, (512, 256), (256, 1))
    assert_size_stride(primals_13, (512,), (1,))
    assert_size_stride(primals_14, (512, 512), (512, 1))
    assert_size_stride(primals_15, (512,), (1,))
    assert_size_stride(primals_16, (512, 512), (512, 1))
    assert_size_stride(primals_17, (512,), (1,))
    assert_size_stride(primals_18, (512, 512), (512, 1))
    assert_size_stride(primals_19, (512,), (1,))
    assert_size_stride(primals_20, (256, 512), (512, 1))
    assert_size_stride(primals_21, (256,), (1,))
    assert_size_stride(primals_22, (128, 256), (256, 1))
    assert_size_stride(primals_23, (128,), (1,))
    assert_size_stride(primals_24, (64, 128), (128, 1))
    assert_size_stride(primals_25, (64,), (1,))
    assert_size_stride(primals_26, (32, 64), (64, 1))
    assert_size_stride(primals_27, (32,), (1,))
    assert_size_stride(primals_28, (3, 32), (32, 1))
    assert_size_stride(primals_29, (3,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 16), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4, 16), (256, 64, 16, 1), torch.bool)
        buf2 = empty_strided_cuda((4, 4, 4, 16), (256, 64, 16, 1), torch.
            float32)
        get_raw_stream(0)
        triton_poi_fused_leaky_relu_0[grid(1024)](buf0, primals_2, buf1,
            buf2, 1024, XBLOCK=256, num_warps=4, num_stages=1)
        del buf0
        del primals_2
        buf3 = empty_strided_cuda((64, 32), (32, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (64, 16), (16, 1), 0),
            reinterpret_tensor(primals_4, (16, 32), (1, 16), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 4, 4, 32), (512, 128, 32, 1), torch.bool)
        buf5 = empty_strided_cuda((4, 4, 4, 32), (512, 128, 32, 1), torch.
            float32)
        triton_poi_fused_leaky_relu_1[grid(2048)](buf3, primals_5, buf4,
            buf5, 2048, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_5
        buf6 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf5, (64, 32), (32, 1), 0),
            reinterpret_tensor(primals_6, (32, 64), (1, 32), 0), out=buf6)
        buf7 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.bool
            )
        buf8 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.
            float32)
        triton_poi_fused_leaky_relu_2[grid(4096)](buf6, primals_7, buf7,
            buf8, 4096, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_7
        buf9 = empty_strided_cuda((64, 128), (128, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf8, (64, 64), (64, 1), 0),
            reinterpret_tensor(primals_8, (64, 128), (1, 64), 0), out=buf9)
        buf10 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1),
            torch.bool)
        buf11 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1),
            torch.float32)
        triton_poi_fused_leaky_relu_3[grid(8192)](buf9, primals_9, buf10,
            buf11, 8192, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_9
        buf12 = empty_strided_cuda((64, 256), (256, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf11, (64, 128), (128, 1), 0),
            reinterpret_tensor(primals_10, (128, 256), (1, 128), 0), out=buf12)
        buf13 = empty_strided_cuda((4, 4, 4, 256), (4096, 1024, 256, 1),
            torch.bool)
        buf14 = empty_strided_cuda((4, 4, 4, 256), (4096, 1024, 256, 1),
            torch.float32)
        triton_poi_fused_leaky_relu_4[grid(16384)](buf12, primals_11, buf13,
            buf14, 16384, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_11
        buf15 = empty_strided_cuda((64, 512), (512, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf14, (64, 256), (256, 1), 0),
            reinterpret_tensor(primals_12, (256, 512), (1, 256), 0), out=buf15)
        buf16 = empty_strided_cuda((4, 4, 4, 512), (8192, 2048, 512, 1),
            torch.bool)
        buf17 = empty_strided_cuda((4, 4, 4, 512), (8192, 2048, 512, 1),
            torch.float32)
        triton_poi_fused_leaky_relu_5[grid(32768)](buf15, primals_13, buf16,
            buf17, 32768, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_13
        buf18 = buf15
        del buf15
        extern_kernels.mm(reinterpret_tensor(buf17, (64, 512), (512, 1), 0),
            reinterpret_tensor(primals_14, (512, 512), (1, 512), 0), out=buf18)
        buf19 = empty_strided_cuda((4, 4, 4, 512), (8192, 2048, 512, 1),
            torch.bool)
        buf20 = empty_strided_cuda((4, 4, 4, 512), (8192, 2048, 512, 1),
            torch.float32)
        triton_poi_fused_leaky_relu_5[grid(32768)](buf18, primals_15, buf19,
            buf20, 32768, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_15
        buf21 = buf18
        del buf18
        extern_kernels.mm(reinterpret_tensor(buf20, (64, 512), (512, 1), 0),
            reinterpret_tensor(primals_16, (512, 512), (1, 512), 0), out=buf21)
        buf22 = empty_strided_cuda((4, 4, 4, 512), (8192, 2048, 512, 1),
            torch.bool)
        buf23 = empty_strided_cuda((4, 4, 4, 512), (8192, 2048, 512, 1),
            torch.float32)
        triton_poi_fused_leaky_relu_5[grid(32768)](buf21, primals_17, buf22,
            buf23, 32768, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_17
        buf24 = buf21
        del buf21
        extern_kernels.mm(reinterpret_tensor(buf23, (64, 512), (512, 1), 0),
            reinterpret_tensor(primals_18, (512, 512), (1, 512), 0), out=buf24)
        buf25 = empty_strided_cuda((4, 4, 4, 512), (8192, 2048, 512, 1),
            torch.bool)
        buf26 = empty_strided_cuda((4, 4, 4, 512), (8192, 2048, 512, 1),
            torch.float32)
        triton_poi_fused_leaky_relu_5[grid(32768)](buf24, primals_19, buf25,
            buf26, 32768, XBLOCK=256, num_warps=4, num_stages=1)
        del buf24
        del primals_19
        buf27 = buf12
        del buf12
        extern_kernels.mm(reinterpret_tensor(buf26, (64, 512), (512, 1), 0),
            reinterpret_tensor(primals_20, (512, 256), (1, 512), 0), out=buf27)
        buf28 = empty_strided_cuda((4, 4, 4, 256), (4096, 1024, 256, 1),
            torch.bool)
        buf29 = empty_strided_cuda((4, 4, 4, 256), (4096, 1024, 256, 1),
            torch.float32)
        triton_poi_fused_leaky_relu_4[grid(16384)](buf27, primals_21, buf28,
            buf29, 16384, XBLOCK=256, num_warps=4, num_stages=1)
        del buf27
        del primals_21
        buf30 = buf9
        del buf9
        extern_kernels.mm(reinterpret_tensor(buf29, (64, 256), (256, 1), 0),
            reinterpret_tensor(primals_22, (256, 128), (1, 256), 0), out=buf30)
        buf31 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1),
            torch.bool)
        buf32 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1),
            torch.float32)
        triton_poi_fused_leaky_relu_3[grid(8192)](buf30, primals_23, buf31,
            buf32, 8192, XBLOCK=256, num_warps=4, num_stages=1)
        del buf30
        del primals_23
        buf33 = buf6
        del buf6
        extern_kernels.mm(reinterpret_tensor(buf32, (64, 128), (128, 1), 0),
            reinterpret_tensor(primals_24, (128, 64), (1, 128), 0), out=buf33)
        buf34 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch
            .bool)
        buf35 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch
            .float32)
        triton_poi_fused_leaky_relu_2[grid(4096)](buf33, primals_25, buf34,
            buf35, 4096, XBLOCK=128, num_warps=4, num_stages=1)
        del buf33
        del primals_25
        buf36 = buf3
        del buf3
        extern_kernels.mm(reinterpret_tensor(buf35, (64, 64), (64, 1), 0),
            reinterpret_tensor(primals_26, (64, 32), (1, 64), 0), out=buf36)
        buf37 = empty_strided_cuda((4, 4, 4, 32), (512, 128, 32, 1), torch.bool
            )
        buf38 = empty_strided_cuda((4, 4, 4, 32), (512, 128, 32, 1), torch.
            float32)
        triton_poi_fused_leaky_relu_1[grid(2048)](buf36, primals_27, buf37,
            buf38, 2048, XBLOCK=256, num_warps=4, num_stages=1)
        del buf36
        del primals_27
        buf39 = empty_strided_cuda((64, 3), (3, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf38, (64, 32), (32, 1), 0),
            reinterpret_tensor(primals_28, (32, 3), (1, 32), 0), out=buf39)
        buf40 = empty_strided_cuda((4, 4, 4, 3), (48, 12, 3, 1), torch.bool)
        buf41 = empty_strided_cuda((4, 4, 4, 3), (48, 12, 3, 1), torch.float32)
        triton_poi_fused_leaky_relu_6[grid(192)](buf39, primals_29, buf40,
            buf41, 192, XBLOCK=128, num_warps=4, num_stages=1)
        del buf39
        del primals_29
    return (buf41, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf1,
        reinterpret_tensor(buf2, (64, 16), (16, 1), 0), buf4,
        reinterpret_tensor(buf5, (64, 32), (32, 1), 0), buf7,
        reinterpret_tensor(buf8, (64, 64), (64, 1), 0), buf10,
        reinterpret_tensor(buf11, (64, 128), (128, 1), 0), buf13,
        reinterpret_tensor(buf14, (64, 256), (256, 1), 0), buf16,
        reinterpret_tensor(buf17, (64, 512), (512, 1), 0), buf19,
        reinterpret_tensor(buf20, (64, 512), (512, 1), 0), buf22,
        reinterpret_tensor(buf23, (64, 512), (512, 1), 0), buf25,
        reinterpret_tensor(buf26, (64, 512), (512, 1), 0), buf28,
        reinterpret_tensor(buf29, (64, 256), (256, 1), 0), buf31,
        reinterpret_tensor(buf32, (64, 128), (128, 1), 0), buf34,
        reinterpret_tensor(buf35, (64, 64), (64, 1), 0), buf37,
        reinterpret_tensor(buf38, (64, 32), (32, 1), 0), buf40, primals_28,
        primals_26, primals_24, primals_22, primals_20, primals_18,
        primals_16, primals_14, primals_12, primals_10, primals_8,
        primals_6, primals_4)


class ModelNew(nn.Module):

    def __init__(self, inputdim):
        super(ModelNew, self).__init__()
        self.layer1 = nn.Linear(inputdim, 16)
        torch.nn.init.xavier_uniform_(self.layer1.weight)
        self.layer2 = nn.Linear(16, 32)
        torch.nn.init.xavier_uniform_(self.layer2.weight)
        self.layer3 = nn.Linear(32, 64)
        torch.nn.init.xavier_uniform_(self.layer3.weight)
        self.layer4 = nn.Linear(64, 128)
        torch.nn.init.xavier_uniform_(self.layer4.weight)
        self.layer5 = nn.Linear(128, 256)
        torch.nn.init.xavier_uniform_(self.layer5.weight)
        self.layer6 = nn.Linear(256, 512)
        torch.nn.init.xavier_uniform_(self.layer6.weight)
        self.layer11 = nn.Linear(512, 512)
        torch.nn.init.xavier_uniform_(self.layer11.weight)
        self.layer12 = nn.Linear(512, 512)
        torch.nn.init.xavier_uniform_(self.layer12.weight)
        self.layer13 = nn.Linear(512, 512)
        torch.nn.init.xavier_uniform_(self.layer13.weight)
        self.layer14 = nn.Linear(512, 256)
        torch.nn.init.xavier_uniform_(self.layer14.weight)
        self.layer7 = nn.Linear(256, 128)
        torch.nn.init.xavier_uniform_(self.layer7.weight)
        self.layer8 = nn.Linear(128, 64)
        torch.nn.init.xavier_uniform_(self.layer8.weight)
        self.layer9 = nn.Linear(64, 32)
        torch.nn.init.xavier_uniform_(self.layer9.weight)
        self.layer10 = nn.Linear(32, 3)
        torch.nn.init.xavier_uniform_(self.layer10.weight)

    def forward(self, input_0):
        primals_1 = self.layer1.weight
        primals_2 = self.layer1.bias
        primals_4 = self.layer2.weight
        primals_5 = self.layer2.bias
        primals_6 = self.layer3.weight
        primals_7 = self.layer3.bias
        primals_8 = self.layer4.weight
        primals_9 = self.layer4.bias
        primals_10 = self.layer5.weight
        primals_11 = self.layer5.bias
        primals_12 = self.layer6.weight
        primals_13 = self.layer6.bias
        primals_14 = self.layer11.weight
        primals_15 = self.layer11.bias
        primals_16 = self.layer12.weight
        primals_17 = self.layer12.bias
        primals_18 = self.layer13.weight
        primals_19 = self.layer13.bias
        primals_20 = self.layer14.weight
        primals_21 = self.layer14.bias
        primals_22 = self.layer7.weight
        primals_23 = self.layer7.bias
        primals_24 = self.layer8.weight
        primals_25 = self.layer8.bias
        primals_26 = self.layer9.weight
        primals_27 = self.layer9.bias
        primals_28 = self.layer10.weight
        primals_29 = self.layer10.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,
            primals_20, primals_21, primals_22, primals_23, primals_24,
            primals_25, primals_26, primals_27, primals_28, primals_29])
        return output[0]
