# 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/na/cnayvtp4eqkhz442k3cjownix7hhd24svsd5geu5lll5pciu5wir.py
# Topologically Sorted Source Nodes: [means, h], Original ATen: [aten.mean, aten._native_batch_norm_legit]
# Source node to ATen node mapping:
#   h => add_1, rsqrt, var_mean
#   means => mean
# Graph fragment:
#   %mean : [num_users=3] = call_function[target=torch.ops.aten.mean.dim](args = (%primals_1, [2, 3]), kwargs = {})
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view, [0, 2, 3]), kwargs = {correction: 0, keepdim: True})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {})
#   %rsqrt : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_1,), kwargs = {})
triton_per_fused__native_batch_norm_legit_mean_0 = async_compile.triton('triton_per_fused__native_batch_norm_legit_mean_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=[16, 16],
    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, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__native_batch_norm_legit_mean_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 5, '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__native_batch_norm_legit_mean_0(in_out_ptr0, in_ptr0, out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    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 = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (16*x0)), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp6 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = tl.full([XBLOCK, 1], 16, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp1 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.where(xmask, tmp15, 0)
    tmp18 = tl.sum(tmp17, 1)[:, None]
    tmp19 = 16.0
    tmp20 = tmp18 / tmp19
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x0), tmp23, xmask)
    tl.store(out_ptr0 + (x0), tmp4, xmask)
    tl.store(out_ptr1 + (x0), tmp12, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/rt/crti7wnji3fr725frh4vjayqrxmykwqwd2lug7drcmcjd2arhxox.py
# Topologically Sorted Source Nodes: [means, m, v, sub, add, sqrt, means_1], Original ATen: [aten.mean, aten.var, aten.sub, aten.add, aten.sqrt, aten.div]
# Source node to ATen node mapping:
#   add => add
#   m => mean_1
#   means => mean
#   means_1 => div
#   sqrt => sqrt
#   sub => sub
#   v => var
# Graph fragment:
#   %mean : [num_users=3] = call_function[target=torch.ops.aten.mean.dim](args = (%primals_1, [2, 3]), kwargs = {})
#   %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%mean, [-1], True), kwargs = {})
#   %var : [num_users=1] = call_function[target=torch.ops.aten.var.correction](args = (%mean, [-1]), kwargs = {correction: 1, keepdim: True})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mean, %mean_1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%var, 1e-05), kwargs = {})
#   %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%add,), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub, %sqrt), kwargs = {})
triton_poi_fused_add_div_mean_sqrt_sub_var_1 = async_compile.triton('triton_poi_fused_add_div_mean_sqrt_sub_var_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=[16], 
    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_add_div_mean_sqrt_sub_var_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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_div_mean_sqrt_sub_var_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp3 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp1 = 16.0
    tmp2 = tmp0 / tmp1
    tmp4 = tmp3 / tmp1
    tmp6 = tmp5 / tmp1
    tmp7 = tmp4 + tmp6
    tmp9 = tmp8 / tmp1
    tmp10 = tmp7 + tmp9
    tmp12 = tmp11 / tmp1
    tmp13 = tmp10 + tmp12
    tmp14 = 4.0
    tmp15 = tmp13 / tmp14
    tmp16 = tmp2 - tmp15
    tmp17 = tmp4 - tmp15
    tmp18 = tmp17 * tmp17
    tmp19 = tmp6 - tmp15
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp22 = tmp9 - tmp15
    tmp23 = tmp22 * tmp22
    tmp24 = tmp21 + tmp23
    tmp25 = tmp12 - tmp15
    tmp26 = tmp25 * tmp25
    tmp27 = tmp24 + tmp26
    tmp28 = 3.0
    tmp29 = tmp27 / tmp28
    tmp30 = 1e-05
    tmp31 = tmp29 + tmp30
    tmp32 = libdevice.sqrt(tmp31)
    tmp33 = tmp16 / tmp32
    tl.store(out_ptr0 + (x2), tmp33, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/f2/cf2ifs7mrq64vpbthqedwiqpt32chlm6npncvlwvjpxxda2t2low.py
# Topologically Sorted Source Nodes: [mul, h_1, mul_1, out], Original ATen: [aten.mul, aten.add]
# Source node to ATen node mapping:
#   h_1 => add_2
#   mul => mul_1
#   mul_1 => mul_2
#   out => add_3
# Graph fragment:
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%unsqueeze_1, %unsqueeze_3), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_1, %mul_1), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_2, %add_2), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_2, %view_3), kwargs = {})
triton_poi_fused_add_mul_2 = async_compile.triton('triton_poi_fused_add_mul_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: '*fp32', 6: '*fp32', 7: '*fp32', 8: '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, 7, 8), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 7, '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_mul_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 16) % 4
    x3 = xindex
    x4 = (xindex // 16)
    tmp0 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x3), xmask)
    tmp2 = tl.load(in_ptr2 + (x4), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr3 + (x4), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr4 + (x4), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr5 + (x1), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr6 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 - tmp2
    tmp5 = tmp3 * tmp4
    tmp8 = tmp6 * tmp7
    tmp9 = tmp5 + tmp8
    tmp10 = tmp0 * tmp9
    tmp12 = tmp10 + tmp11
    tl.store(out_ptr0 + (x3), tmp12, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, ), (1, ))
    assert_size_stride(primals_4, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf2 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 1, 1), torch.float32)
        buf3 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32)
        buf5 = reinterpret_tensor(buf3, (1, 16, 1, 1), (16, 1, 1, 1), 0); del buf3  # reuse
        # Topologically Sorted Source Nodes: [means, h], Original ATen: [aten.mean, aten._native_batch_norm_legit]
        stream0 = get_raw_stream(0)
        triton_per_fused__native_batch_norm_legit_mean_0.run(buf5, primals_1, buf0, buf2, 16, 16, grid=grid(16), stream=stream0)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [means, m, v, sub, add, sqrt, means_1], Original ATen: [aten.mean, aten.var, aten.sub, aten.add, aten.sqrt, aten.div]
        triton_poi_fused_add_div_mean_sqrt_sub_var_1.run(buf0, buf1, 16, grid=grid(16), stream=stream0)
        del buf0
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul, h_1, mul_1, out], Original ATen: [aten.mul, aten.add]
        triton_poi_fused_add_mul_2.run(primals_3, primals_1, buf2, buf5, buf1, primals_2, primals_4, buf6, 256, grid=grid(256), stream=stream0)
        del primals_4
    return (buf6, primals_1, primals_2, primals_3, buf2, buf5, reinterpret_tensor(buf1, (4, 4, 1, 1), (4, 1, 1, 1), 0), )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
    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 InstanceNorm2dPlus(nn.Module):

    def __init__(self, num_features, bias=True):
        super().__init__()
        self.num_features = num_features
        self.bias = bias
        self.instance_norm = nn.InstanceNorm2d(num_features, affine=False,
            track_running_stats=False)
        self.alpha = nn.Parameter(torch.zeros(num_features))
        self.gamma = nn.Parameter(torch.zeros(num_features))
        self.alpha.data.normal_(1, 0.02)
        self.gamma.data.normal_(1, 0.02)
        if bias:
            self.beta = nn.Parameter(torch.zeros(num_features))

    def forward(self, x):
        means = torch.mean(x, dim=(2, 3))
        m = torch.mean(means, dim=-1, keepdim=True)
        v = torch.var(means, dim=-1, keepdim=True)
        means = (means - m) / torch.sqrt(v + 1e-05)
        h = self.instance_norm(x)
        if self.bias:
            h = h + means[..., None, None] * self.alpha[..., None, None]
            out = self.gamma.view(-1, self.num_features, 1, 1
                ) * h + self.beta.view(-1, self.num_features, 1, 1)
        else:
            h = h + means[..., None, None] * self.alpha[..., None, None]
            out = self.gamma.view(-1, self.num_features, 1, 1) * h
        return out


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


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

import torch
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.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_per_fused__native_batch_norm_legit_mean_0(in_out_ptr0, in_ptr0,
    out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp6 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = tl.full([XBLOCK, 1], 16, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp1 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.where(xmask, tmp15, 0)
    tmp18 = tl.sum(tmp17, 1)[:, None]
    tmp19 = 16.0
    tmp20 = tmp18 / tmp19
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp23, xmask)
    tl.store(out_ptr0 + x0, tmp4, xmask)
    tl.store(out_ptr1 + x0, tmp12, xmask)


@triton.jit
def triton_poi_fused_add_div_mean_sqrt_sub_var_1(in_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp3 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp1 = 16.0
    tmp2 = tmp0 / tmp1
    tmp4 = tmp3 / tmp1
    tmp6 = tmp5 / tmp1
    tmp7 = tmp4 + tmp6
    tmp9 = tmp8 / tmp1
    tmp10 = tmp7 + tmp9
    tmp12 = tmp11 / tmp1
    tmp13 = tmp10 + tmp12
    tmp14 = 4.0
    tmp15 = tmp13 / tmp14
    tmp16 = tmp2 - tmp15
    tmp17 = tmp4 - tmp15
    tmp18 = tmp17 * tmp17
    tmp19 = tmp6 - tmp15
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp22 = tmp9 - tmp15
    tmp23 = tmp22 * tmp22
    tmp24 = tmp21 + tmp23
    tmp25 = tmp12 - tmp15
    tmp26 = tmp25 * tmp25
    tmp27 = tmp24 + tmp26
    tmp28 = 3.0
    tmp29 = tmp27 / tmp28
    tmp30 = 1e-05
    tmp31 = tmp29 + tmp30
    tmp32 = libdevice.sqrt(tmp31)
    tmp33 = tmp16 / tmp32
    tl.store(out_ptr0 + x2, tmp33, xmask)


@triton.jit
def triton_poi_fused_add_mul_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    in_ptr5, in_ptr6, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 4
    x3 = xindex
    x4 = xindex // 16
    tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x3, xmask)
    tmp2 = tl.load(in_ptr2 + x4, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr3 + x4, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr4 + x4, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr5 + x1, xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr6 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 - tmp2
    tmp5 = tmp3 * tmp4
    tmp8 = tmp6 * tmp7
    tmp9 = tmp5 + tmp8
    tmp10 = tmp0 * tmp9
    tmp12 = tmp10 + tmp11
    tl.store(out_ptr0 + x3, tmp12, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf2 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 1, 1), torch.float32)
        buf3 = empty_strided_cuda((1, 16, 1, 1), (16, 1, 16, 16), torch.float32
            )
        buf5 = reinterpret_tensor(buf3, (1, 16, 1, 1), (16, 1, 1, 1), 0)
        del buf3
        get_raw_stream(0)
        triton_per_fused__native_batch_norm_legit_mean_0[grid(16)](buf5,
            primals_1, buf0, buf2, 16, 16, XBLOCK=1, num_warps=2, num_stages=1)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_add_div_mean_sqrt_sub_var_1[grid(16)](buf0, buf1, 
            16, XBLOCK=16, num_warps=1, num_stages=1)
        del buf0
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_mul_2[grid(256)](primals_3, primals_1, buf2,
            buf5, buf1, primals_2, primals_4, buf6, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del primals_4
    return (buf6, primals_1, primals_2, primals_3, buf2, buf5,
        reinterpret_tensor(buf1, (4, 4, 1, 1), (4, 1, 1, 1), 0))


class InstanceNorm2dPlusNew(nn.Module):

    def __init__(self, num_features, bias=True):
        super().__init__()
        self.num_features = num_features
        self.bias = bias
        self.instance_norm = nn.InstanceNorm2d(num_features, affine=False,
            track_running_stats=False)
        self.alpha = nn.Parameter(torch.zeros(num_features))
        self.gamma = nn.Parameter(torch.zeros(num_features))
        self.alpha.data.normal_(1, 0.02)
        self.gamma.data.normal_(1, 0.02)
        if bias:
            self.beta = nn.Parameter(torch.zeros(num_features))

    def forward(self, input_0):
        primals_2 = self.alpha
        primals_3 = self.gamma
        primals_4 = self.beta
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
