# AOT ID: ['0_inference']
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_6/inductor_cache/hv/chvlig3vkflhzd7ivdbf6ojzalx44fop7l6z3mhozziz64jmw5ck.py
# Topologically Sorted Source Nodes: [mul, sum_2, cnt, cnt_for_mu, mu, sub, sigma, mul_1, sum_3, sub_1, cnt_fot_sigma, sigma_1, add, sigma_2], Original ATen: [aten.mul, aten.sum, aten.clamp, aten.div, aten.sub, aten.pow, aten.add, aten.sqrt]
# Source node to ATen node mapping:
#   add => add
#   cnt => sum_1
#   cnt_for_mu => clamp_max, clamp_min
#   cnt_fot_sigma => clamp_max_1, clamp_min_1
#   mu => div
#   mul => mul
#   mul_1 => mul_1
#   sigma => pow_1
#   sigma_1 => div_1
#   sigma_2 => sqrt
#   sub => sub
#   sub_1 => sub_1
#   sum_2 => sum_2
#   sum_3 => sum_3
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg1_1, %unsqueeze), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [-1], True), kwargs = {})
#   %sum_1 : [num_users=2] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%unsqueeze, [-1], True), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sum_1, 1.0), kwargs = {})
#   %clamp_max : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 100000.0), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_2, %clamp_max), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg1_1, %div), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_1, %unsqueeze), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_1, [-1], True), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sum_1, 1), kwargs = {})
#   %clamp_min_1 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub_1, 1.0), kwargs = {})
#   %clamp_max_1 : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min_1, 100000.0), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_3, %clamp_max_1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%div_1, 1e-08), kwargs = {})
#   %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%add,), kwargs = {})
triton_poi_fused_add_clamp_div_mul_pow_sqrt_sub_sum_0 = async_compile.triton('triton_poi_fused_add_clamp_div_mul_pow_sqrt_sub_sum_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 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_add_clamp_div_mul_pow_sqrt_sub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 8, '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_clamp_div_mul_pow_sqrt_sub_sum_0(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex % 64
    x0 = xindex % 16
    x2 = (xindex // 64)
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (4*x3), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + ((4*x0) + (64*x2)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + (4*x3)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (1 + (4*x0) + (64*x2)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + (4*x3)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (2 + (4*x0) + (64*x2)), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + (4*x3)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (3 + (4*x0) + (64*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tmp15 = tmp1 + tmp4
    tmp16 = tmp15 + tmp8
    tmp17 = tmp16 + tmp12
    tmp18 = 1.0
    tmp19 = triton_helpers.maximum(tmp17, tmp18)
    tmp20 = 100000.0
    tmp21 = triton_helpers.minimum(tmp19, tmp20)
    tmp22 = tmp14 / tmp21
    tmp23 = tmp0 - tmp22
    tmp24 = tmp23 * tmp23
    tmp25 = tmp24 * tmp1
    tmp26 = tmp3 - tmp22
    tmp27 = tmp26 * tmp26
    tmp28 = tmp27 * tmp4
    tmp29 = tmp25 + tmp28
    tmp30 = tmp7 - tmp22
    tmp31 = tmp30 * tmp30
    tmp32 = tmp31 * tmp8
    tmp33 = tmp29 + tmp32
    tmp34 = tmp11 - tmp22
    tmp35 = tmp34 * tmp34
    tmp36 = tmp35 * tmp12
    tmp37 = tmp33 + tmp36
    tmp38 = tmp17 - tmp18
    tmp39 = triton_helpers.maximum(tmp38, tmp18)
    tmp40 = triton_helpers.minimum(tmp39, tmp20)
    tmp41 = tmp37 / tmp40
    tmp42 = 1e-08
    tmp43 = tmp41 + tmp42
    tmp44 = libdevice.sqrt(tmp43)
    tl.store(out_ptr0 + (x4), tmp22, xmask)
    tl.store(in_out_ptr0 + (x4), tmp44, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/x7/cx7htd3xitvxaxetefd5p23n7ecwnggzfyxx3t47rzzxl37p6cg2.py
# Topologically Sorted Source Nodes: [cnt, sub_2, sub_1, cnt_fot_sigma, sigma_1, add, sigma_2, y], Original ATen: [aten.sum, aten.sub, aten.clamp, aten.div, aten.add, aten.sqrt]
# Source node to ATen node mapping:
#   add => add
#   cnt => sum_1
#   cnt_fot_sigma => clamp_max_1, clamp_min_1
#   sigma_1 => div_1
#   sigma_2 => sqrt
#   sub_1 => sub_1
#   sub_2 => sub_2
#   y => div_2
# Graph fragment:
#   %sum_1 : [num_users=2] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%unsqueeze, [-1], True), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg1_1, %div), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sum_1, 1), kwargs = {})
#   %clamp_min_1 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub_1, 1.0), kwargs = {})
#   %clamp_max_1 : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min_1, 100000.0), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_3, %clamp_max_1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%div_1, 1e-08), kwargs = {})
#   %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%add,), kwargs = {})
#   %div_2 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub_2, %sqrt), kwargs = {})
triton_poi_fused_add_clamp_div_sqrt_sub_sum_1 = async_compile.triton('triton_poi_fused_add_clamp_div_sqrt_sub_sum_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=[1024], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 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_add_clamp_div_sqrt_sub_sum_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_clamp_div_sqrt_sub_sum_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex % 256
    x4 = (xindex // 4)
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (x3), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x4), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x4), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 / tmp3
    tl.store(out_ptr0 + (x5), tmp4, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4, 1), (64, 16, 4, 1, 256), torch.float32)
        buf1 = empty_strided_cuda((4, 4, 4, 4, 1), (64, 16, 4, 1, 256), torch.float32)
        buf2 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [mul, sum_2, cnt, cnt_for_mu, mu, sub, sigma, mul_1, sum_3, sub_1, cnt_fot_sigma, sigma_1, add, sigma_2], Original ATen: [aten.mul, aten.sum, aten.clamp, aten.div, aten.sub, aten.pow, aten.add, aten.sqrt]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_clamp_div_mul_pow_sqrt_sub_sum_0.run(buf2, arg1_1, arg0_1, buf0, 256, grid=grid(256), stream=stream0)
        del arg0_1
        buf3 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cnt, sub_2, sub_1, cnt_fot_sigma, sigma_1, add, sigma_2, y], Original ATen: [aten.sum, aten.sub, aten.clamp, aten.div, aten.add, aten.sqrt]
        triton_poi_fused_add_clamp_div_sqrt_sub_sum_1.run(arg1_1, buf0, buf2, buf3, 1024, grid=grid(1024), stream=stream0)
        del arg1_1
        del buf0
        del buf2
    return (buf3, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1])
    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.cuda
from torch import nn
import torch.distributed
import torch.utils.data
import torch.optim


class MaskedInstanceNorm1d(nn.Module):
    """Instance norm + masking."""
    MAX_CNT = 100000.0

    def __init__(self, d_channel: 'int', unbiased: 'bool'=True, affine:
        'bool'=False):
        super().__init__()
        self.d_channel = d_channel
        self.unbiased = unbiased
        self.affine = affine
        if self.affine:
            gamma = torch.ones(d_channel, dtype=torch.float)
            beta = torch.zeros_like(gamma)
            self.register_parameter('gamma', nn.Parameter(gamma))
            self.register_parameter('beta', nn.Parameter(beta))

    def forward(self, x: 'torch.Tensor', x_mask: 'torch.Tensor'
        ) ->torch.Tensor:
        """`x`: [B,C,T], `x_mask`: [B,T] => [B,C,T]."""
        x_mask = x_mask.unsqueeze(1).type_as(x)
        cnt = x_mask.sum(dim=-1, keepdim=True)
        cnt_for_mu = cnt.clamp(1.0, self.MAX_CNT)
        mu = (x * x_mask).sum(dim=-1, keepdim=True) / cnt_for_mu
        sigma = (x - mu) ** 2
        cnt_fot_sigma = (cnt - int(self.unbiased)).clamp(1.0, self.MAX_CNT)
        sigma = (sigma * x_mask).sum(dim=-1, keepdim=True) / cnt_fot_sigma
        sigma = (sigma + 1e-08).sqrt()
        y = (x - mu) / sigma
        if self.affine:
            gamma = self.gamma.unsqueeze(0).unsqueeze(-1)
            beta = self.beta.unsqueeze(0).unsqueeze(-1)
            y = y * gamma + beta
        return y


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


def get_init_inputs():
    return [[], {'d_channel': 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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.cuda
from torch import nn
import torch.distributed
import torch.utils.data
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_add_clamp_div_mul_pow_sqrt_sub_sum_0(in_out_ptr0,
    in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex % 64
    x0 = xindex % 16
    x2 = xindex // 64
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x3, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (4 * x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x3), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (1 + 4 * x0 + 64 * x2), xmask, eviction_policy
        ='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + 4 * x3), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (2 + 4 * x0 + 64 * x2), xmask, eviction_policy
        ='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + 4 * x3), xmask, eviction_policy='evict_last'
        )
    tmp12 = tl.load(in_ptr1 + (3 + 4 * x0 + 64 * x2), xmask,
        eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tmp15 = tmp1 + tmp4
    tmp16 = tmp15 + tmp8
    tmp17 = tmp16 + tmp12
    tmp18 = 1.0
    tmp19 = triton_helpers.maximum(tmp17, tmp18)
    tmp20 = 100000.0
    tmp21 = triton_helpers.minimum(tmp19, tmp20)
    tmp22 = tmp14 / tmp21
    tmp23 = tmp0 - tmp22
    tmp24 = tmp23 * tmp23
    tmp25 = tmp24 * tmp1
    tmp26 = tmp3 - tmp22
    tmp27 = tmp26 * tmp26
    tmp28 = tmp27 * tmp4
    tmp29 = tmp25 + tmp28
    tmp30 = tmp7 - tmp22
    tmp31 = tmp30 * tmp30
    tmp32 = tmp31 * tmp8
    tmp33 = tmp29 + tmp32
    tmp34 = tmp11 - tmp22
    tmp35 = tmp34 * tmp34
    tmp36 = tmp35 * tmp12
    tmp37 = tmp33 + tmp36
    tmp38 = tmp17 - tmp18
    tmp39 = triton_helpers.maximum(tmp38, tmp18)
    tmp40 = triton_helpers.minimum(tmp39, tmp20)
    tmp41 = tmp37 / tmp40
    tmp42 = 1e-08
    tmp43 = tmp41 + tmp42
    tmp44 = libdevice.sqrt(tmp43)
    tl.store(out_ptr0 + x4, tmp22, xmask)
    tl.store(in_out_ptr0 + x4, tmp44, xmask)


@triton.jit
def triton_poi_fused_add_clamp_div_sqrt_sub_sum_1(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex % 256
    x4 = xindex // 4
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x4, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x4, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 / tmp3
    tl.store(out_ptr0 + x5, tmp4, xmask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4, 1), (64, 16, 4, 1, 256),
            torch.float32)
        buf1 = empty_strided_cuda((4, 4, 4, 4, 1), (64, 16, 4, 1, 256),
            torch.float32)
        buf2 = buf1
        del buf1
        get_raw_stream(0)
        triton_poi_fused_add_clamp_div_mul_pow_sqrt_sub_sum_0[grid(256)](buf2,
            arg1_1, arg0_1, buf0, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
        buf3 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        triton_poi_fused_add_clamp_div_sqrt_sub_sum_1[grid(1024)](arg1_1,
            buf0, buf2, buf3, 1024, XBLOCK=128, num_warps=4, num_stages=1)
        del arg1_1
        del buf0
        del buf2
    return buf3,


class MaskedInstanceNorm1dNew(nn.Module):
    """Instance norm + masking."""
    MAX_CNT = 100000.0

    def __init__(self, d_channel: 'int', unbiased: 'bool'=True, affine:
        'bool'=False):
        super().__init__()
        self.d_channel = d_channel
        self.unbiased = unbiased
        self.affine = affine
        if self.affine:
            gamma = torch.ones(d_channel, dtype=torch.float)
            beta = torch.zeros_like(gamma)
            self.register_parameter('gamma', nn.Parameter(gamma))
            self.register_parameter('beta', nn.Parameter(beta))

    def forward(self, input_0, input_1):
        arg0_1 = input_0
        arg1_1 = input_1
        output = call([arg0_1, arg1_1])
        return output[0]
