# 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_2/inductor_cache/o4/co4wraift36owelzgugohszydvmwmur7is3xtdmrderi6a6eiigs.py
# Topologically Sorted Source Nodes: [x_1, out, stack], Original ATen: [aten.clamp, aten.mean, aten.stack]
# Source node to ATen node mapping:
#   out => mean
#   stack => cat
#   x_1 => clamp_min
# Graph fragment:
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%select, 1e-06), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%clamp_min, [-1, -2], True), kwargs = {})
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%unsqueeze, %unsqueeze_1, %unsqueeze_2, %unsqueeze_3], -1), kwargs = {})
triton_per_fused_clamp_mean_stack_0 = async_compile.triton('triton_per_fused_clamp_mean_stack_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, 16],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_clamp_mean_stack_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 1, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_clamp_mean_stack_0(in_ptr0, out_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    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 = 1e-06
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.where(xmask, tmp3, 0)
    tmp6 = tl.sum(tmp5, 1)[:, None]
    tmp7 = 16.0
    tmp8 = tmp6 / tmp7
    tl.store(out_ptr1 + (4*x0), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_2/inductor_cache/7v/c7vv7g5ye5glwbrdqc4fqle557oeimklablethxngzrg55s7g4j7.py
# Topologically Sorted Source Nodes: [x_3, out_1, stack], Original ATen: [aten.clamp, aten.mean, aten.stack]
# Source node to ATen node mapping:
#   out_1 => mean_1
#   stack => cat
#   x_3 => clamp_min_1
# Graph fragment:
#   %clamp_min_1 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%select_1, 1e-06), kwargs = {})
#   %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%clamp_min_1, [-1, -2], True), kwargs = {})
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%unsqueeze, %unsqueeze_1, %unsqueeze_2, %unsqueeze_3], -1), kwargs = {})
triton_per_fused_clamp_mean_stack_1 = async_compile.triton('triton_per_fused_clamp_mean_stack_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.persistent_reduction(
    size_hints=[4, 16],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_clamp_mean_stack_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 1, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_clamp_mean_stack_1(in_ptr0, out_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    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 + (64 + r1 + (16*x0)), xmask, other=0.0)
    tmp1 = 1e-06
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.where(xmask, tmp3, 0)
    tmp6 = tl.sum(tmp5, 1)[:, None]
    tmp7 = 16.0
    tmp8 = tmp6 / tmp7
    tl.store(out_ptr1 + (4*x0), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_2/inductor_cache/iw/ciwbmr7shlxnhuevspti3jblz2vwjzmpavr6ytw7swzufqkntxmx.py
# Topologically Sorted Source Nodes: [x_5, out_2, stack], Original ATen: [aten.clamp, aten.mean, aten.stack]
# Source node to ATen node mapping:
#   out_2 => mean_2
#   stack => cat
#   x_5 => clamp_min_2
# Graph fragment:
#   %clamp_min_2 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%select_2, 1e-06), kwargs = {})
#   %mean_2 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%clamp_min_2, [-1, -2], True), kwargs = {})
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%unsqueeze, %unsqueeze_1, %unsqueeze_2, %unsqueeze_3], -1), kwargs = {})
triton_per_fused_clamp_mean_stack_2 = async_compile.triton('triton_per_fused_clamp_mean_stack_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.persistent_reduction(
    size_hints=[4, 16],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_clamp_mean_stack_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 1, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_clamp_mean_stack_2(in_ptr0, out_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    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 + (128 + r1 + (16*x0)), xmask, other=0.0)
    tmp1 = 1e-06
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.where(xmask, tmp3, 0)
    tmp6 = tl.sum(tmp5, 1)[:, None]
    tmp7 = 16.0
    tmp8 = tmp6 / tmp7
    tl.store(out_ptr1 + (4*x0), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_2/inductor_cache/lf/clfdgvdrt3m36jarehsgif2ecimchh377y62pjyfob34eeoiankj.py
# Topologically Sorted Source Nodes: [x_7, out_3, stack], Original ATen: [aten.clamp, aten.mean, aten.stack]
# Source node to ATen node mapping:
#   out_3 => mean_3
#   stack => cat
#   x_7 => clamp_min_3
# Graph fragment:
#   %clamp_min_3 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%select_3, 1e-06), kwargs = {})
#   %mean_3 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%clamp_min_3, [-1, -2], True), kwargs = {})
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%unsqueeze, %unsqueeze_1, %unsqueeze_2, %unsqueeze_3], -1), kwargs = {})
triton_per_fused_clamp_mean_stack_3 = async_compile.triton('triton_per_fused_clamp_mean_stack_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.persistent_reduction(
    size_hints=[4, 16],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_clamp_mean_stack_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 1, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_clamp_mean_stack_3(in_ptr0, out_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    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 + (192 + r1 + (16*x0)), xmask, other=0.0)
    tmp1 = 1e-06
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.where(xmask, tmp3, 0)
    tmp6 = tl.sum(tmp5, 1)[:, None]
    tmp7 = 16.0
    tmp8 = tmp6 / tmp7
    tl.store(out_ptr1 + (4*x0), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_2/inductor_cache/yr/cyr5yowerghujulrhhw3y5arjz6r2g6t4mtgh3655epqcq3rmxp5.py
# Topologically Sorted Source Nodes: [mean], Original ATen: [aten.mean]
# Source node to ATen node mapping:
#   mean => mean_4
# Graph fragment:
#   %mean_4 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%cat, [-1]), kwargs = {})
triton_poi_fused_mean_4 = async_compile.triton('triton_poi_fused_mean_4', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[4], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mean_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_mean_4(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 + (4*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tl.store(out_ptr0 + (x0), tmp8, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf8 = empty_strided_cuda((4, 1, 1, 4), (4, 1, 4, 1), torch.float32)
        buf4 = reinterpret_tensor(buf8, (4, 1, 1, 1), (4, 1, 4, 1), 0)  # alias
        # Topologically Sorted Source Nodes: [x_1, out, stack], Original ATen: [aten.clamp, aten.mean, aten.stack]
        stream0 = get_raw_stream(0)
        triton_per_fused_clamp_mean_stack_0.run(arg0_1, buf4, 4, 16, grid=grid(4), stream=stream0)
        buf5 = reinterpret_tensor(buf8, (4, 1, 1, 1), (4, 1, 4, 1), 1)  # alias
        # Topologically Sorted Source Nodes: [x_3, out_1, stack], Original ATen: [aten.clamp, aten.mean, aten.stack]
        triton_per_fused_clamp_mean_stack_1.run(arg0_1, buf5, 4, 16, grid=grid(4), stream=stream0)
        buf6 = reinterpret_tensor(buf8, (4, 1, 1, 1), (4, 1, 4, 1), 2)  # alias
        # Topologically Sorted Source Nodes: [x_5, out_2, stack], Original ATen: [aten.clamp, aten.mean, aten.stack]
        triton_per_fused_clamp_mean_stack_2.run(arg0_1, buf6, 4, 16, grid=grid(4), stream=stream0)
        buf7 = reinterpret_tensor(buf8, (4, 1, 1, 1), (4, 1, 4, 1), 3)  # alias
        # Topologically Sorted Source Nodes: [x_7, out_3, stack], Original ATen: [aten.clamp, aten.mean, aten.stack]
        triton_per_fused_clamp_mean_stack_3.run(arg0_1, buf7, 4, 16, grid=grid(4), stream=stream0)
        del arg0_1
        buf9 = empty_strided_cuda((4, 1, 1), (1, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mean], Original ATen: [aten.mean]
        triton_poi_fused_mean_4.run(buf8, buf9, 4, grid=grid(4), stream=stream0)
        del buf4
        del buf5
        del buf6
        del buf7
        del buf8
    return (buf9, )


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)
    fn = lambda: call([arg0_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 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
import torch.nn as nn
from abc import ABC
import torch.autograd
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_clamp_mean_stack_0(in_ptr0, out_ptr1, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    xnumel = 4
    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 = 1e-06
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.where(xmask, tmp3, 0)
    tmp6 = tl.sum(tmp5, 1)[:, None]
    tmp7 = 16.0
    tmp8 = tmp6 / tmp7
    tl.store(out_ptr1 + 4 * x0, tmp8, xmask)


@triton.jit
def triton_per_fused_clamp_mean_stack_1(in_ptr0, out_ptr1, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    xnumel = 4
    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 + (64 + r1 + 16 * x0), xmask, other=0.0)
    tmp1 = 1e-06
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.where(xmask, tmp3, 0)
    tmp6 = tl.sum(tmp5, 1)[:, None]
    tmp7 = 16.0
    tmp8 = tmp6 / tmp7
    tl.store(out_ptr1 + 4 * x0, tmp8, xmask)


@triton.jit
def triton_per_fused_clamp_mean_stack_2(in_ptr0, out_ptr1, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    xnumel = 4
    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 + (128 + r1 + 16 * x0), xmask, other=0.0)
    tmp1 = 1e-06
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.where(xmask, tmp3, 0)
    tmp6 = tl.sum(tmp5, 1)[:, None]
    tmp7 = 16.0
    tmp8 = tmp6 / tmp7
    tl.store(out_ptr1 + 4 * x0, tmp8, xmask)


@triton.jit
def triton_per_fused_clamp_mean_stack_3(in_ptr0, out_ptr1, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    xnumel = 4
    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 + (192 + r1 + 16 * x0), xmask, other=0.0)
    tmp1 = 1e-06
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.where(xmask, tmp3, 0)
    tmp6 = tl.sum(tmp5, 1)[:, None]
    tmp7 = 16.0
    tmp8 = tmp6 / tmp7
    tl.store(out_ptr1 + 4 * x0, tmp8, xmask)


@triton.jit
def triton_poi_fused_mean_4(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 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tl.store(out_ptr0 + x0, tmp8, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf8 = empty_strided_cuda((4, 1, 1, 4), (4, 1, 4, 1), torch.float32)
        buf4 = reinterpret_tensor(buf8, (4, 1, 1, 1), (4, 1, 4, 1), 0)
        get_raw_stream(0)
        triton_per_fused_clamp_mean_stack_0[grid(4)](arg0_1, buf4, 4, 16,
            XBLOCK=1, num_warps=2, num_stages=1)
        buf5 = reinterpret_tensor(buf8, (4, 1, 1, 1), (4, 1, 4, 1), 1)
        triton_per_fused_clamp_mean_stack_1[grid(4)](arg0_1, buf5, 4, 16,
            XBLOCK=1, num_warps=2, num_stages=1)
        buf6 = reinterpret_tensor(buf8, (4, 1, 1, 1), (4, 1, 4, 1), 2)
        triton_per_fused_clamp_mean_stack_2[grid(4)](arg0_1, buf6, 4, 16,
            XBLOCK=1, num_warps=2, num_stages=1)
        buf7 = reinterpret_tensor(buf8, (4, 1, 1, 1), (4, 1, 4, 1), 3)
        triton_per_fused_clamp_mean_stack_3[grid(4)](arg0_1, buf7, 4, 16,
            XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        buf9 = empty_strided_cuda((4, 1, 1), (1, 1, 1), torch.float32)
        triton_poi_fused_mean_4[grid(4)](buf8, buf9, 4, XBLOCK=4, num_warps
            =1, num_stages=1)
        del buf4
        del buf5
        del buf6
        del buf7
        del buf8
    return buf9,


class GeneralizedMeanPoolingListNew(nn.Module, ABC):
    """Applies a 2D power-average adaptive pooling over an input signal composed of
    several input planes.
    The function computed is: :math:`f(X) = pow(sum(pow(X, p)), 1/p)`
        - At p = infinity, one gets Max Pooling
        - At p = 1, one gets Average Pooling
    The output is of size H x W, for any input size.
    The number of output features is equal to the number of input planes.
    Args:
        output_size: the target output size of the image of the form H x W.
                     Can be a tuple (H, W) or a single H for a square image H x H
                     H and W can be either a ``int``, or ``None`` which means the size
                     will be the same as that of the input.
    """

    def __init__(self, output_size=1, eps=1e-06):
        super(GeneralizedMeanPoolingListNew, self).__init__()
        self.output_size = output_size
        self.eps = eps

    def __repr__(self):
        return self.__class__.__name__ + '(' + 'output_size=' + str(self.
            output_size) + ')'

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