# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_4/inductor_cache/ow/cowdcudxhul3bkp5n6x4sh5xlfsr5wjeys5mle367nouiee2xmcv.py
# Topologically Sorted Source Nodes: [var, pow_1, sub], Original ATen: [aten.clamp, aten.pow, aten.sub]
# Source node to ATen node mapping:
#   pow_1 => pow_1
#   sub => sub
#   var => clamp_min
# Graph fragment:
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%primals_1, 3.814697265625e-06), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%clamp_min, 2), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%pow_1, 1.4551915228366852e-11), kwargs = {})
triton_poi_fused_clamp_pow_sub_0 = async_compile.triton('triton_poi_fused_clamp_pow_sub_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=[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_clamp_pow_sub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_clamp_pow_sub_0(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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = 3.814697265625e-06
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = tmp2 * tmp2
    tmp4 = 1.4551915228366852e-11
    tmp5 = tmp3 - tmp4
    tl.store(out_ptr0 + (x0), tmp5, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/2y/c2y2depfxa2z6x54ydtq4sztgglwdvphffk5xy2ad6meezomjm6h.py
# Topologically Sorted Source Nodes: [pow_3], Original ATen: [aten.pow]
# Source node to ATen node mapping:
#   pow_3 => pow_3
# Graph fragment:
#   %pow_3 : [num_users=2] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%primals_3, 2), kwargs = {})
triton_poi_fused_pow_1 = async_compile.triton('triton_poi_fused_pow_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: '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_pow_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_pow_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = tmp0 * tmp0
    tl.store(out_ptr0 + (x0), tmp1, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ug/cug44ghn7hpw52fmb6cvsqsikmnucfhvsokiyh4jncwkntkhgtb3.py
# Topologically Sorted Source Nodes: [var_1, pow_2, beta, norm_pool], Original ATen: [aten.clamp, aten.pow, aten.sub, aten.convolution]
# Source node to ATen node mapping:
#   beta => sub_1
#   norm_pool => convolution
#   pow_2 => pow_2
#   var_1 => clamp_min_1
# Graph fragment:
#   %clamp_min_1 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%primals_2, 0.0010000072759311445), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%clamp_min_1, 2), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%pow_2, 1.4551915228366852e-11), kwargs = {})
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%pow_3, %view, %sub_1, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_clamp_convolution_pow_sub_2 = async_compile.triton('triton_poi_fused_clamp_convolution_pow_sub_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=[4], 
    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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clamp_convolution_pow_sub_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_clamp_convolution_pow_sub_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = 0.0010000072759311445
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = tmp2 * tmp2
    tmp4 = 1.4551915228366852e-11
    tmp5 = tmp3 - tmp4
    tl.store(out_ptr0 + (x0), tmp5, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/cl/cclwght2uzu6dkoinyev2wpfgnp3tg3nmwinz7z2d5yrlspj6gh5.py
# Topologically Sorted Source Nodes: [var_1, pow_2, beta, norm_pool, norm_pool_1, norm_pool_2], Original ATen: [aten.clamp, aten.pow, aten.sub, aten.convolution, aten.sqrt, aten.div]
# Source node to ATen node mapping:
#   beta => sub_1
#   norm_pool => convolution
#   norm_pool_1 => sqrt
#   norm_pool_2 => div
#   pow_2 => pow_2
#   var_1 => clamp_min_1
# Graph fragment:
#   %clamp_min_1 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%primals_2, 0.0010000072759311445), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%clamp_min_1, 2), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%pow_2, 1.4551915228366852e-11), kwargs = {})
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%pow_3, %view, %sub_1, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%convolution,), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%primals_3, %sqrt), kwargs = {})
triton_poi_fused_clamp_convolution_div_pow_sqrt_sub_3 = async_compile.triton('triton_poi_fused_clamp_convolution_div_pow_sqrt_sub_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=[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_clamp_convolution_div_pow_sqrt_sub_3', 'mutated_arg_names': ['in_out_ptr0'], '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_clamp_convolution_div_pow_sqrt_sub_3(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
    x1 = (xindex // 16) % 4
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (x3), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = libdevice.sqrt(tmp2)
    tmp5 = tmp3 / tmp4
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
    tl.store(out_ptr0 + (x3), tmp5, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (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, 1), torch.float32)
        # Topologically Sorted Source Nodes: [var, pow_1, sub], Original ATen: [aten.clamp, aten.pow, aten.sub]
        stream0 = get_raw_stream(0)
        triton_poi_fused_clamp_pow_sub_0.run(primals_1, buf0, 16, grid=grid(16), stream=stream0)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [pow_3], Original ATen: [aten.pow]
        triton_poi_fused_pow_1.run(primals_3, buf1, 256, grid=grid(256), stream=stream0)
        buf2 = empty_strided_cuda((4, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [var_1, pow_2, beta, norm_pool], Original ATen: [aten.clamp, aten.pow, aten.sub, aten.convolution]
        triton_poi_fused_clamp_convolution_pow_sub_2.run(primals_2, buf2, 4, grid=grid(4), stream=stream0)
        # Topologically Sorted Source Nodes: [var_1, pow_2, beta, norm_pool], Original ATen: [aten.clamp, aten.pow, aten.sub, aten.convolution]
        buf3 = extern_kernels.convolution(buf1, reinterpret_tensor(buf0, (4, 4, 1, 1), (4, 1, 0, 0), 0), stride=(1, 1), padding=(0, 0), 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 = buf3; del buf3  # reuse
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [var_1, pow_2, beta, norm_pool, norm_pool_1, norm_pool_2], Original ATen: [aten.clamp, aten.pow, aten.sub, aten.convolution, aten.sqrt, aten.div]
        triton_poi_fused_clamp_convolution_div_pow_sqrt_sub_3.run(buf4, buf2, primals_3, buf5, 256, grid=grid(256), stream=stream0)
        del buf2
    return (buf5, primals_1, primals_2, primals_3, reinterpret_tensor(buf0, (4, 4, 1, 1), (4, 1, 1, 1), 0), buf1, buf4, )


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, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (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)
    fn = lambda: call([primals_1, primals_2, primals_3])
    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)

from torch.autograd import Function
import torch
import torch.nn.functional as F
import torch.nn as nn


class LowerBound(Function):

    @staticmethod
    def forward(ctx, inputs, bound):
        ctx.save_for_backward(inputs, inputs.new_ones(1) * bound)
        return inputs.clamp(min=bound)

    @staticmethod
    def backward(ctx, grad_output):
        inputs, bound = ctx.saved_tensors
        pass_through_1 = inputs >= bound
        pass_through_2 = grad_output < 0
        pass_through = pass_through_1 | pass_through_2
        return pass_through.type(grad_output.dtype) * grad_output, None


class GDN(nn.Module):

    def __init__(self, num_features, inverse=False, gamma_init=0.1,
        beta_bound=1e-06, gamma_bound=0.0, reparam_offset=2 ** -18):
        super(GDN, self).__init__()
        self._inverse = inverse
        self.num_features = num_features
        self.reparam_offset = reparam_offset
        self.pedestal = self.reparam_offset ** 2
        beta_init = torch.sqrt(torch.ones(num_features, dtype=torch.float) +
            self.pedestal)
        gama_init = torch.sqrt(torch.full((num_features, num_features),
            fill_value=gamma_init, dtype=torch.float) * torch.eye(
            num_features, dtype=torch.float) + self.pedestal)
        self.beta = nn.Parameter(beta_init)
        self.gamma = nn.Parameter(gama_init)
        self.beta_bound = (beta_bound + self.pedestal) ** 0.5
        self.gamma_bound = (gamma_bound + self.pedestal) ** 0.5

    def _reparam(self, var, bound):
        var = LowerBound.apply(var, bound)
        return var ** 2 - self.pedestal

    def forward(self, x):
        gamma = self._reparam(self.gamma, self.gamma_bound).view(self.
            num_features, self.num_features, 1, 1)
        beta = self._reparam(self.beta, self.beta_bound)
        norm_pool = F.conv2d(x ** 2, gamma, bias=beta, stride=1, padding=0)
        norm_pool = torch.sqrt(norm_pool)
        if self._inverse:
            norm_pool = x * norm_pool
        else:
            norm_pool = x / norm_pool
        return norm_pool


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


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

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
from torch.autograd import Function
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_clamp_pow_sub_0(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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 3.814697265625e-06
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = tmp2 * tmp2
    tmp4 = 1.4551915228366852e-11
    tmp5 = tmp3 - tmp4
    tl.store(out_ptr0 + x0, tmp5, xmask)


@triton.jit
def triton_poi_fused_pow_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tmp0 * tmp0
    tl.store(out_ptr0 + x0, tmp1, xmask)


@triton.jit
def triton_poi_fused_clamp_convolution_pow_sub_2(in_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.0010000072759311445
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = tmp2 * tmp2
    tmp4 = 1.4551915228366852e-11
    tmp5 = tmp3 - tmp4
    tl.store(out_ptr0 + x0, tmp5, xmask)


@triton.jit
def triton_poi_fused_clamp_convolution_div_pow_sqrt_sub_3(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
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x3, xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = libdevice.sqrt(tmp2)
    tmp5 = tmp3 / tmp4
    tl.store(in_out_ptr0 + x3, tmp2, xmask)
    tl.store(out_ptr0 + x3, tmp5, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (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, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clamp_pow_sub_0[grid(16)](primals_1, buf0, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_pow_1[grid(256)](primals_3, buf1, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf2 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_poi_fused_clamp_convolution_pow_sub_2[grid(4)](primals_2,
            buf2, 4, XBLOCK=4, num_warps=1, num_stages=1)
        buf3 = extern_kernels.convolution(buf1, reinterpret_tensor(buf0, (4,
            4, 1, 1), (4, 1, 0, 0), 0), stride=(1, 1), padding=(0, 0),
            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 = buf3
        del buf3
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clamp_convolution_div_pow_sqrt_sub_3[grid(256)](buf4,
            buf2, primals_3, buf5, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del buf2
    return buf5, primals_1, primals_2, primals_3, reinterpret_tensor(buf0,
        (4, 4, 1, 1), (4, 1, 1, 1), 0), buf1, buf4


class LowerBound(Function):

    @staticmethod
    def forward(ctx, inputs, bound):
        ctx.save_for_backward(inputs, inputs.new_ones(1) * bound)
        return inputs.clamp(min=bound)

    @staticmethod
    def backward(ctx, grad_output):
        inputs, bound = ctx.saved_tensors
        pass_through_1 = inputs >= bound
        pass_through_2 = grad_output < 0
        pass_through = pass_through_1 | pass_through_2
        return pass_through.type(grad_output.dtype) * grad_output, None


class GDNNew(nn.Module):

    def __init__(self, num_features, inverse=False, gamma_init=0.1,
        beta_bound=1e-06, gamma_bound=0.0, reparam_offset=2 ** -18):
        super(GDNNew, self).__init__()
        self._inverse = inverse
        self.num_features = num_features
        self.reparam_offset = reparam_offset
        self.pedestal = self.reparam_offset ** 2
        beta_init = torch.sqrt(torch.ones(num_features, dtype=torch.float) +
            self.pedestal)
        gama_init = torch.sqrt(torch.full((num_features, num_features),
            fill_value=gamma_init, dtype=torch.float) * torch.eye(
            num_features, dtype=torch.float) + self.pedestal)
        self.beta = nn.Parameter(beta_init)
        self.gamma = nn.Parameter(gama_init)
        self.beta_bound = (beta_bound + self.pedestal) ** 0.5
        self.gamma_bound = (gamma_bound + self.pedestal) ** 0.5

    def _reparam(self, var, bound):
        var = LowerBound.apply(var, bound)
        return var ** 2 - self.pedestal

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