# 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_8/inductor_cache/p7/cp7mjs4cvfmmk3xanldfhbfyw3iz6jtioejhqhvyiw5cvteee5uz.py
# Topologically Sorted Source Nodes: [_weight_norm], Original ATen: [aten._weight_norm_interface]
# Source node to ATen node mapping:
#   _weight_norm => div, mul, pow_1, pow_2, sum_1
# Graph fragment:
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%primals_2, 2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [1, 2, 3], True), kwargs = {})
#   %pow_2 : [num_users=2] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_1, 0.5), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%primals_1, %pow_2), kwargs = {})
#   %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, %div), kwargs = {})
triton_per_fused__weight_norm_interface_0 = async_compile.triton('triton_per_fused__weight_norm_interface_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.persistent_reduction(
    size_hints=[4, 64],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__weight_norm_interface_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 1, '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}
)
@triton.jit
def triton_per_fused__weight_norm_interface_0(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    rnumel = 36
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = rindex < rnumel
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (36*x0)), rmask & xmask, other=0.0)
    tmp7 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.where(rmask & xmask, tmp2, 0)
    tmp5 = tl.sum(tmp4, 1)[:, None]
    tmp6 = libdevice.sqrt(tmp5)
    tmp8 = tmp7 / tmp6
    tmp9 = tmp0 * tmp8
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x0), tmp6, xmask)
    tl.store(out_ptr0 + (r1 + (36*x0)), tmp9, rmask & xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/nv/cnvdbfyfdbxtpncclnbabjm26fof5xpzppwtrmvdrw7jk2nf4pwe.py
# Topologically Sorted Source Nodes: [conv2d, sub, relu, x], Original ATen: [aten.convolution, aten.sub, aten.relu, aten.add, aten.threshold_backward]
# Source node to ATen node mapping:
#   conv2d => convolution
#   relu => relu
#   sub => sub
#   x => add
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_4, %mul, %primals_3, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%convolution, %primals_5), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%sub,), kwargs = {})
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%relu, %primals_5), kwargs = {})
#   %le_1 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_add_convolution_relu_sub_threshold_backward_1 = async_compile.triton('triton_poi_fused_add_convolution_relu_sub_threshold_backward_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*i1', 5: '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, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_relu_sub_threshold_backward_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_add_convolution_relu_sub_threshold_backward_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 16) % 4
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (0))
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
    tmp2 = tmp0 + tmp1
    tmp5 = tmp2 - tmp4
    tmp6 = tl.full([1], 0, tl.int32)
    tmp7 = triton_helpers.maximum(tmp6, tmp5)
    tmp8 = tmp7 + tmp4
    tmp9 = 0.0
    tmp10 = tmp7 <= tmp9
    tl.store(out_ptr0 + (x3), tmp8, xmask)
    tl.store(out_ptr1 + (x3), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/rw/crwhyerteo5xwfa3sfmuoxn746ux3ykv2a4bvfxwb75n543lkk3a.py
# Topologically Sorted Source Nodes: [out, out_1, sub_1, relu_1, x_1], Original ATen: [aten.convolution, aten.add, aten.sub, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   out => convolution_1
#   out_1 => add_1
#   relu_1 => relu_1
#   sub_1 => sub_1
#   x_1 => add_2
# Graph fragment:
#   %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%add, %mul_1, %primals_8, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution_1, %primals_4), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_1, %primals_9), kwargs = {})
#   %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%sub_1,), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%relu_1, %primals_9), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_1, 0), kwargs = {})
triton_poi_fused_add_convolution_relu_sub_threshold_backward_2 = async_compile.triton('triton_poi_fused_add_convolution_relu_sub_threshold_backward_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*i1', 6: '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, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_relu_sub_threshold_backward_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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_add_convolution_relu_sub_threshold_backward_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 16) % 4
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x3), xmask)
    tmp5 = tl.load(in_ptr3 + (0))
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp7 = tmp4 - tmp6
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tmp10 = tmp9 + tmp6
    tmp11 = 0.0
    tmp12 = tmp9 <= tmp11
    tl.store(out_ptr0 + (x3), tmp10, xmask)
    tl.store(out_ptr1 + (x3), tmp12, 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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 1, 1, 1), (1, 1, 1, 1))
    assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (1, ), (1, ))
    assert_size_stride(primals_6, (4, 1, 1, 1), (1, 1, 1, 1))
    assert_size_stride(primals_7, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_8, (4, ), (1, ))
    assert_size_stride(primals_9, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        buf1 = reinterpret_tensor(buf0, (4, 1, 1, 1), (1, 1, 1, 1), 0); del buf0  # reuse
        buf2 = empty_strided_cuda((4, 4, 3, 3), (36, 9, 3, 1), torch.float32)
        # Topologically Sorted Source Nodes: [_weight_norm], Original ATen: [aten._weight_norm_interface]
        stream0 = get_raw_stream(0)
        triton_per_fused__weight_norm_interface_0.run(buf1, primals_2, primals_1, buf2, 4, 36, grid=grid(4), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(primals_4, buf2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1))
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf11 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d, sub, relu, x], Original ATen: [aten.convolution, aten.sub, aten.relu, aten.add, aten.threshold_backward]
        triton_poi_fused_add_convolution_relu_sub_threshold_backward_1.run(buf3, primals_3, primals_5, buf4, buf11, 256, grid=grid(256), stream=stream0)
        del primals_3
        del primals_5
        buf5 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        buf6 = reinterpret_tensor(buf5, (4, 1, 1, 1), (1, 1, 1, 1), 0); del buf5  # reuse
        buf7 = empty_strided_cuda((4, 4, 3, 3), (36, 9, 3, 1), torch.float32)
        # Topologically Sorted Source Nodes: [_weight_norm_1], Original ATen: [aten._weight_norm_interface]
        triton_per_fused__weight_norm_interface_0.run(buf6, primals_7, primals_6, buf7, 4, 36, grid=grid(4), stream=stream0)
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.convolution]
        buf8 = extern_kernels.convolution(buf4, buf7, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 4, 4, 4), (64, 16, 4, 1))
        buf9 = buf3; del buf3  # reuse
        buf10 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [out, out_1, sub_1, relu_1, x_1], Original ATen: [aten.convolution, aten.add, aten.sub, aten.relu, aten.threshold_backward]
        triton_poi_fused_add_convolution_relu_sub_threshold_backward_2.run(buf8, primals_8, primals_4, primals_9, buf9, buf10, 256, grid=grid(256), stream=stream0)
        del buf8
        del primals_8
        del primals_9
    return (buf9, buf2, buf7, primals_1, primals_2, primals_4, primals_6, primals_7, buf1, buf2, buf4, buf6, buf7, buf10, buf11, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 1, 1, 1), (1, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 1, 1, 1), (1, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9])
    return print_performance(fn, times=times, repeat=repeat)


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

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.utils.weight_norm as weightNorm


def conv3x3(in_planes, out_planes, stride=1):
    return weightNorm(nn.Conv2d(in_planes, out_planes, kernel_size=3,
        stride=stride, padding=1, bias=True))


class TReLU(nn.Module):

    def __init__(self):
        super(TReLU, self).__init__()
        self.alpha = nn.Parameter(torch.FloatTensor(1), requires_grad=True)
        self.alpha.data.fill_(0)

    def forward(self, x):
        x = F.relu(x - self.alpha) + self.alpha
        return x


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_planes, planes, stride=1):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(in_planes, planes, stride)
        self.conv2 = conv3x3(planes, planes)
        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion * planes:
            self.shortcut = nn.Sequential(weightNorm(nn.Conv2d(in_planes, 
                self.expansion * planes, kernel_size=1, stride=stride, bias
                =True)))
        self.relu_1 = TReLU()
        self.relu_2 = TReLU()

    def forward(self, x):
        out = self.relu_1(self.conv1(x))
        out = self.conv2(out)
        out += self.shortcut(x)
        out = self.relu_2(out)
        return out


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


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

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.utils.weight_norm as weightNorm
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_per_fused__weight_norm_interface_0(in_out_ptr0, in_ptr0, in_ptr1,
    out_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    rnumel = 36
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    rmask = rindex < rnumel
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 36 * x0), rmask & xmask, other=0.0)
    tmp7 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.where(rmask & xmask, tmp2, 0)
    tmp5 = tl.sum(tmp4, 1)[:, None]
    tmp6 = libdevice.sqrt(tmp5)
    tmp8 = tmp7 / tmp6
    tmp9 = tmp0 * tmp8
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp6, xmask)
    tl.store(out_ptr0 + (r1 + 36 * x0), tmp9, rmask & xmask)


@triton.jit
def triton_poi_fused_add_convolution_relu_sub_threshold_backward_1(in_ptr0,
    in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + 0)
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
    tmp2 = tmp0 + tmp1
    tmp5 = tmp2 - tmp4
    tmp6 = tl.full([1], 0, tl.int32)
    tmp7 = triton_helpers.maximum(tmp6, tmp5)
    tmp8 = tmp7 + tmp4
    tmp9 = 0.0
    tmp10 = tmp7 <= tmp9
    tl.store(out_ptr0 + x3, tmp8, xmask)
    tl.store(out_ptr1 + x3, tmp10, xmask)


@triton.jit
def triton_poi_fused_add_convolution_relu_sub_threshold_backward_2(in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x3, xmask)
    tmp5 = tl.load(in_ptr3 + 0)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp7 = tmp4 - tmp6
    tmp8 = tl.full([1], 0, tl.int32)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tmp10 = tmp9 + tmp6
    tmp11 = 0.0
    tmp12 = tmp9 <= tmp11
    tl.store(out_ptr0 + x3, tmp10, xmask)
    tl.store(out_ptr1 + x3, tmp12, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 1, 1, 1), (1, 1, 1, 1))
    assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (1,), (1,))
    assert_size_stride(primals_6, (4, 1, 1, 1), (1, 1, 1, 1))
    assert_size_stride(primals_7, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        buf1 = reinterpret_tensor(buf0, (4, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf0
        buf2 = empty_strided_cuda((4, 4, 3, 3), (36, 9, 3, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused__weight_norm_interface_0[grid(4)](buf1, primals_2,
            primals_1, buf2, 4, 36, XBLOCK=1, num_warps=2, num_stages=1)
        buf3 = extern_kernels.convolution(primals_4, buf2, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1))
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf11 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_add_convolution_relu_sub_threshold_backward_1[grid
            (256)](buf3, primals_3, primals_5, buf4, buf11, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_3
        del primals_5
        buf5 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        buf6 = reinterpret_tensor(buf5, (4, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf5
        buf7 = empty_strided_cuda((4, 4, 3, 3), (36, 9, 3, 1), torch.float32)
        triton_per_fused__weight_norm_interface_0[grid(4)](buf6, primals_7,
            primals_6, buf7, 4, 36, XBLOCK=1, num_warps=2, num_stages=1)
        buf8 = extern_kernels.convolution(buf4, buf7, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 4, 4, 4), (64, 16, 4, 1))
        buf9 = buf3
        del buf3
        buf10 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_add_convolution_relu_sub_threshold_backward_2[grid
            (256)](buf8, primals_8, primals_4, primals_9, buf9, buf10, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del buf8
        del primals_8
        del primals_9
    return (buf9, buf2, buf7, primals_1, primals_2, primals_4, primals_6,
        primals_7, buf1, buf2, buf4, buf6, buf7, buf10, buf11)


def conv3x3(in_planes, out_planes, stride=1):
    return weightNorm(nn.Conv2d(in_planes, out_planes, kernel_size=3,
        stride=stride, padding=1, bias=True))


class TReLU(nn.Module):

    def __init__(self):
        super(TReLU, self).__init__()
        self.alpha = nn.Parameter(torch.FloatTensor(1), requires_grad=True)
        self.alpha.data.fill_(0)

    def forward(self, x):
        x = F.relu(x - self.alpha) + self.alpha
        return x


class BasicBlockNew(nn.Module):
    expansion = 1

    def __init__(self, in_planes, planes, stride=1):
        super(BasicBlockNew, self).__init__()
        self.conv1 = conv3x3(in_planes, planes, stride)
        self.conv2 = conv3x3(planes, planes)
        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion * planes:
            self.shortcut = nn.Sequential(weightNorm(nn.Conv2d(in_planes, 
                self.expansion * planes, kernel_size=1, stride=stride, bias
                =True)))
        self.relu_1 = TReLU()
        self.relu_2 = TReLU()

    def forward(self, input_0):
        primals_3 = self.conv1.bias
        primals_1 = self.conv1.weight_g
        primals_2 = self.conv1.weight_v
        primals_8 = self.conv2.bias
        primals_6 = self.conv2.weight_g
        primals_7 = self.conv2.weight_v
        primals_5 = self.relu_1.alpha
        primals_9 = self.relu_2.alpha
        primals_4 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0]
