# 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_1/inductor_cache/kf/ckfcpf3khjtz423uvqdkzk324dhtgsfjvpyzbxdb75fhdqayhofh.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.avg_pool2d]
# Source node to ATen node mapping:
#   x => avg_pool2d
# Graph fragment:
#   %avg_pool2d : [num_users=1] = call_function[target=torch.ops.aten.avg_pool2d.default](args = (%arg0_1, [3, 3], [2, 2], [1, 1]), kwargs = {})
triton_poi_fused_avg_pool2d_0 = async_compile.triton('triton_poi_fused_avg_pool2d_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=[64], 
    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, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_avg_pool2d_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 9, '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_avg_pool2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 2) % 2
    x0 = xindex % 2
    x3 = (xindex // 2)
    x4 = xindex
    tmp0 = (-1) + (2*x1)
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = (-1) + (2*x0)
    tmp7 = tmp6 >= tmp1
    tmp8 = tmp6 < tmp3
    tmp9 = tmp7 & tmp8
    tmp10 = tmp5 & tmp9
    tmp11 = tl.load(in_ptr0 + ((-5) + (2*x0) + (8*x3)), tmp10 & xmask, eviction_policy='evict_last', other=0.0)
    tmp12 = 2*x0
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp13 & tmp14
    tmp16 = tmp5 & tmp15
    tmp17 = tl.load(in_ptr0 + ((-4) + (2*x0) + (8*x3)), tmp16 & xmask, eviction_policy='evict_last', other=0.0)
    tmp18 = tmp17 + tmp11
    tmp19 = 1 + (2*x0)
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp20 & tmp21
    tmp23 = tmp5 & tmp22
    tmp24 = tl.load(in_ptr0 + ((-3) + (2*x0) + (8*x3)), tmp23 & xmask, eviction_policy='evict_last', other=0.0)
    tmp25 = tmp24 + tmp18
    tmp26 = 2*x1
    tmp27 = tmp26 >= tmp1
    tmp28 = tmp26 < tmp3
    tmp29 = tmp27 & tmp28
    tmp30 = tmp29 & tmp9
    tmp31 = tl.load(in_ptr0 + ((-1) + (2*x0) + (8*x3)), tmp30 & xmask, eviction_policy='evict_last', other=0.0)
    tmp32 = tmp31 + tmp25
    tmp33 = tmp29 & tmp15
    tmp34 = tl.load(in_ptr0 + ((2*x0) + (8*x3)), tmp33 & xmask, eviction_policy='evict_last', other=0.0)
    tmp35 = tmp34 + tmp32
    tmp36 = tmp29 & tmp22
    tmp37 = tl.load(in_ptr0 + (1 + (2*x0) + (8*x3)), tmp36 & xmask, eviction_policy='evict_last', other=0.0)
    tmp38 = tmp37 + tmp35
    tmp39 = 1 + (2*x1)
    tmp40 = tmp39 >= tmp1
    tmp41 = tmp39 < tmp3
    tmp42 = tmp40 & tmp41
    tmp43 = tmp42 & tmp9
    tmp44 = tl.load(in_ptr0 + (3 + (2*x0) + (8*x3)), tmp43 & xmask, eviction_policy='evict_last', other=0.0)
    tmp45 = tmp44 + tmp38
    tmp46 = tmp42 & tmp15
    tmp47 = tl.load(in_ptr0 + (4 + (2*x0) + (8*x3)), tmp46 & xmask, eviction_policy='evict_last', other=0.0)
    tmp48 = tmp47 + tmp45
    tmp49 = tmp42 & tmp22
    tmp50 = tl.load(in_ptr0 + (5 + (2*x0) + (8*x3)), tmp49 & xmask, eviction_policy='evict_last', other=0.0)
    tmp51 = tmp50 + tmp48
    tmp52 = 1 + ((-2)*x0) + ((-2)*x1) + (((5) * ((5) <= (2 + (2*x0))) + (2 + (2*x0)) * ((2 + (2*x0)) < (5)))*((5) * ((5) <= (2 + (2*x1))) + (2 + (2*x1)) * ((2 + (2*x1)) < (5)))) + ((-2)*x0*((5) * ((5) <= (2 + (2*x1))) + (2 + (2*x1)) * ((2 + (2*x1)) < (5)))) + ((-2)*x1*((5) * ((5) <= (2 + (2*x0))) + (2 + (2*x0)) * ((2 + (2*x0)) < (5)))) + (4*x0*x1) + ((5) * ((5) <= (2 + (2*x0))) + (2 + (2*x0)) * ((2 + (2*x0)) < (5))) + ((5) * ((5) <= (2 + (2*x1))) + (2 + (2*x1)) * ((2 + (2*x1)) < (5)))
    tmp53 = tmp51 / tmp52
    tl.store(out_ptr0 + (x4), tmp53, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/wu/cwu44mumxnpsp4plppncxkcov4p3blxsihnfyxkvhoqokhmxxzcn.py
# Topologically Sorted Source Nodes: [x_1, x_2, x_3], Original ATen: [aten.avg_pool2d]
# Source node to ATen node mapping:
#   x_1 => avg_pool2d_1
#   x_2 => avg_pool2d_2
#   x_3 => avg_pool2d_3
# Graph fragment:
#   %avg_pool2d_1 : [num_users=1] = call_function[target=torch.ops.aten.avg_pool2d.default](args = (%avg_pool2d, [3, 3], [2, 2], [1, 1]), kwargs = {})
#   %avg_pool2d_2 : [num_users=1] = call_function[target=torch.ops.aten.avg_pool2d.default](args = (%avg_pool2d_1, [3, 3], [2, 2], [1, 1]), kwargs = {})
#   %avg_pool2d_3 : [num_users=1] = call_function[target=torch.ops.aten.avg_pool2d.default](args = (%avg_pool2d_2, [3, 3], [2, 2], [1, 1]), kwargs = {})
triton_poi_fused_avg_pool2d_1 = async_compile.triton('triton_poi_fused_avg_pool2d_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=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, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_avg_pool2d_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 9, '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_avg_pool2d_1(in_out_ptr0, in_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.full([1], -1, tl.int64)
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 2, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = tmp5 & tmp5
    tmp7 = tl.load(in_ptr0 + ((-3) + (4*x0)), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
    tmp8 = tmp1 >= tmp1
    tmp9 = tmp1 < tmp3
    tmp10 = tmp8 & tmp9
    tmp11 = tmp5 & tmp10
    tmp12 = tl.load(in_ptr0 + ((-2) + (4*x0)), tmp11 & xmask, eviction_policy='evict_last', other=0.0)
    tmp13 = tmp12 + tmp7
    tmp14 = tl.full([1], 1, tl.int64)
    tmp15 = tmp14 >= tmp1
    tmp16 = tmp14 < tmp3
    tmp17 = tmp15 & tmp16
    tmp18 = tmp5 & tmp17
    tmp19 = tl.load(in_ptr0 + ((-1) + (4*x0)), tmp18 & xmask, eviction_policy='evict_last', other=0.0)
    tmp20 = tmp19 + tmp13
    tmp21 = tmp10 & tmp5
    tmp22 = tl.load(in_ptr0 + ((-1) + (4*x0)), tmp21 & xmask, eviction_policy='evict_last', other=0.0)
    tmp23 = tmp22 + tmp20
    tmp24 = tmp10 & tmp10
    tmp25 = tl.load(in_ptr0 + (4*x0), tmp24 & xmask, eviction_policy='evict_last', other=0.0)
    tmp26 = tmp25 + tmp23
    tmp27 = tmp10 & tmp17
    tmp28 = tl.load(in_ptr0 + (1 + (4*x0)), tmp27 & xmask, eviction_policy='evict_last', other=0.0)
    tmp29 = tmp28 + tmp26
    tmp30 = tmp17 & tmp5
    tmp31 = tl.load(in_ptr0 + (1 + (4*x0)), tmp30 & xmask, eviction_policy='evict_last', other=0.0)
    tmp32 = tmp31 + tmp29
    tmp33 = tmp17 & tmp10
    tmp34 = tl.load(in_ptr0 + (2 + (4*x0)), tmp33 & xmask, eviction_policy='evict_last', other=0.0)
    tmp35 = tmp34 + tmp32
    tmp36 = tmp17 & tmp17
    tmp37 = tl.load(in_ptr0 + (3 + (4*x0)), tmp36 & xmask, eviction_policy='evict_last', other=0.0)
    tmp38 = tmp37 + tmp35
    tmp39 = tl.full([1], 9, tl.int32)
    tmp40 = tmp38 / tmp39
    tmp41 = tmp0 < tmp14
    tmp42 = tmp2 & tmp41
    tmp43 = tmp42 & tmp42
    tmp44 = tmp1 < tmp14
    tmp45 = tmp8 & tmp44
    tmp46 = tmp42 & tmp45
    tmp47 = tmp40 + tmp40
    tmp48 = tmp14 < tmp14
    tmp49 = tmp15 & tmp48
    tmp50 = tmp42 & tmp49
    tmp51 = tmp40 + tmp47
    tmp52 = tmp45 & tmp42
    tmp53 = tmp40 + tmp51
    tmp54 = tmp45 & tmp45
    tmp55 = tmp40 + tmp53
    tmp56 = tmp45 & tmp49
    tmp57 = tmp40 + tmp55
    tmp58 = tmp49 & tmp42
    tmp59 = tmp40 + tmp57
    tmp60 = tmp49 & tmp45
    tmp61 = tmp40 + tmp59
    tmp62 = tmp49 & tmp49
    tmp63 = tmp40 + tmp61
    tmp64 = tmp63 / tmp39
    tmp65 = tmp64 + tmp64
    tmp66 = tmp64 + tmp65
    tmp67 = tmp64 + tmp66
    tmp68 = tmp64 + tmp67
    tmp69 = tmp64 + tmp68
    tmp70 = tmp64 + tmp69
    tmp71 = tmp64 + tmp70
    tmp72 = tmp64 + tmp71
    tmp73 = tmp72 / tmp39
    tl.store(in_out_ptr0 + (x0), tmp73, 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)
        buf0 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.avg_pool2d]
        stream0 = get_raw_stream(0)
        triton_poi_fused_avg_pool2d_0.run(arg0_1, buf0, 64, grid=grid(64), stream=stream0)
        del arg0_1
        buf1 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        buf2 = buf1; del buf1  # reuse
        buf3 = reinterpret_tensor(buf2, (4, 4, 1, 1), (4, 1, 1, 1), 0); del buf2  # reuse
        # Topologically Sorted Source Nodes: [x_1, x_2, x_3], Original ATen: [aten.avg_pool2d]
        triton_poi_fused_avg_pool2d_1.run(buf3, buf0, 16, grid=grid(16), stream=stream0)
        del buf0
    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)
    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 torch.nn as nn
import torch._C
import torch.serialization


class InputInjection(nn.Module):
    """Downsampling module for CGNet."""

    def __init__(self, num_downsampling):
        super(InputInjection, self).__init__()
        self.pool = nn.ModuleList()
        for i in range(num_downsampling):
            self.pool.append(nn.AvgPool2d(3, stride=2, padding=1))

    def forward(self, x):
        for pool in self.pool:
            x = pool(x)
        return x


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


def get_init_inputs():
    return [[], {'num_downsampling': 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
import torch.nn as nn
import torch._C
import torch.serialization
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_avg_pool2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 2 % 2
    x0 = xindex % 2
    x3 = xindex // 2
    x4 = xindex
    tmp0 = -1 + 2 * x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = -1 + 2 * x0
    tmp7 = tmp6 >= tmp1
    tmp8 = tmp6 < tmp3
    tmp9 = tmp7 & tmp8
    tmp10 = tmp5 & tmp9
    tmp11 = tl.load(in_ptr0 + (-5 + 2 * x0 + 8 * x3), tmp10 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp12 = 2 * x0
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp13 & tmp14
    tmp16 = tmp5 & tmp15
    tmp17 = tl.load(in_ptr0 + (-4 + 2 * x0 + 8 * x3), tmp16 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp18 = tmp17 + tmp11
    tmp19 = 1 + 2 * x0
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp20 & tmp21
    tmp23 = tmp5 & tmp22
    tmp24 = tl.load(in_ptr0 + (-3 + 2 * x0 + 8 * x3), tmp23 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp25 = tmp24 + tmp18
    tmp26 = 2 * x1
    tmp27 = tmp26 >= tmp1
    tmp28 = tmp26 < tmp3
    tmp29 = tmp27 & tmp28
    tmp30 = tmp29 & tmp9
    tmp31 = tl.load(in_ptr0 + (-1 + 2 * x0 + 8 * x3), tmp30 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp32 = tmp31 + tmp25
    tmp33 = tmp29 & tmp15
    tmp34 = tl.load(in_ptr0 + (2 * x0 + 8 * x3), tmp33 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp35 = tmp34 + tmp32
    tmp36 = tmp29 & tmp22
    tmp37 = tl.load(in_ptr0 + (1 + 2 * x0 + 8 * x3), tmp36 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp38 = tmp37 + tmp35
    tmp39 = 1 + 2 * x1
    tmp40 = tmp39 >= tmp1
    tmp41 = tmp39 < tmp3
    tmp42 = tmp40 & tmp41
    tmp43 = tmp42 & tmp9
    tmp44 = tl.load(in_ptr0 + (3 + 2 * x0 + 8 * x3), tmp43 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp45 = tmp44 + tmp38
    tmp46 = tmp42 & tmp15
    tmp47 = tl.load(in_ptr0 + (4 + 2 * x0 + 8 * x3), tmp46 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp48 = tmp47 + tmp45
    tmp49 = tmp42 & tmp22
    tmp50 = tl.load(in_ptr0 + (5 + 2 * x0 + 8 * x3), tmp49 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp51 = tmp50 + tmp48
    tmp52 = 1 + -2 * x0 + -2 * x1 + (5 * (5 <= 2 + 2 * x0) + (2 + 2 * x0) *
        (2 + 2 * x0 < 5)) * (5 * (5 <= 2 + 2 * x1) + (2 + 2 * x1) * (2 + 2 *
        x1 < 5)) + -2 * x0 * (5 * (5 <= 2 + 2 * x1) + (2 + 2 * x1) * (2 + 2 *
        x1 < 5)) + -2 * x1 * (5 * (5 <= 2 + 2 * x0) + (2 + 2 * x0) * (2 + 2 *
        x0 < 5)) + 4 * x0 * x1 + (5 * (5 <= 2 + 2 * x0) + (2 + 2 * x0) * (2 +
        2 * x0 < 5)) + (5 * (5 <= 2 + 2 * x1) + (2 + 2 * x1) * (2 + 2 * x1 < 5)
        )
    tmp53 = tmp51 / tmp52
    tl.store(out_ptr0 + x4, tmp53, xmask)


@triton.jit
def triton_poi_fused_avg_pool2d_1(in_out_ptr0, in_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.full([1], -1, tl.int64)
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 2, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = tmp5 & tmp5
    tmp7 = tl.load(in_ptr0 + (-3 + 4 * x0), tmp6 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp8 = tmp1 >= tmp1
    tmp9 = tmp1 < tmp3
    tmp10 = tmp8 & tmp9
    tmp11 = tmp5 & tmp10
    tmp12 = tl.load(in_ptr0 + (-2 + 4 * x0), tmp11 & xmask, eviction_policy
        ='evict_last', other=0.0)
    tmp13 = tmp12 + tmp7
    tmp14 = tl.full([1], 1, tl.int64)
    tmp15 = tmp14 >= tmp1
    tmp16 = tmp14 < tmp3
    tmp17 = tmp15 & tmp16
    tmp18 = tmp5 & tmp17
    tmp19 = tl.load(in_ptr0 + (-1 + 4 * x0), tmp18 & xmask, eviction_policy
        ='evict_last', other=0.0)
    tmp20 = tmp19 + tmp13
    tmp21 = tmp10 & tmp5
    tmp22 = tl.load(in_ptr0 + (-1 + 4 * x0), tmp21 & xmask, eviction_policy
        ='evict_last', other=0.0)
    tmp23 = tmp22 + tmp20
    tmp24 = tmp10 & tmp10
    tmp25 = tl.load(in_ptr0 + 4 * x0, tmp24 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp26 = tmp25 + tmp23
    tmp27 = tmp10 & tmp17
    tmp28 = tl.load(in_ptr0 + (1 + 4 * x0), tmp27 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp29 = tmp28 + tmp26
    tmp30 = tmp17 & tmp5
    tmp31 = tl.load(in_ptr0 + (1 + 4 * x0), tmp30 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp32 = tmp31 + tmp29
    tmp33 = tmp17 & tmp10
    tmp34 = tl.load(in_ptr0 + (2 + 4 * x0), tmp33 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp35 = tmp34 + tmp32
    tmp36 = tmp17 & tmp17
    tmp37 = tl.load(in_ptr0 + (3 + 4 * x0), tmp36 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp38 = tmp37 + tmp35
    tmp39 = tl.full([1], 9, tl.int32)
    tmp40 = tmp38 / tmp39
    tmp41 = tmp0 < tmp14
    tmp42 = tmp2 & tmp41
    tmp42 & tmp42
    tmp44 = tmp1 < tmp14
    tmp45 = tmp8 & tmp44
    tmp42 & tmp45
    tmp47 = tmp40 + tmp40
    tmp48 = tmp14 < tmp14
    tmp49 = tmp15 & tmp48
    tmp42 & tmp49
    tmp51 = tmp40 + tmp47
    tmp45 & tmp42
    tmp53 = tmp40 + tmp51
    tmp45 & tmp45
    tmp55 = tmp40 + tmp53
    tmp45 & tmp49
    tmp57 = tmp40 + tmp55
    tmp49 & tmp42
    tmp59 = tmp40 + tmp57
    tmp49 & tmp45
    tmp61 = tmp40 + tmp59
    tmp49 & tmp49
    tmp63 = tmp40 + tmp61
    tmp64 = tmp63 / tmp39
    tmp65 = tmp64 + tmp64
    tmp66 = tmp64 + tmp65
    tmp67 = tmp64 + tmp66
    tmp68 = tmp64 + tmp67
    tmp69 = tmp64 + tmp68
    tmp70 = tmp64 + tmp69
    tmp71 = tmp64 + tmp70
    tmp72 = tmp64 + tmp71
    tmp73 = tmp72 / tmp39
    tl.store(in_out_ptr0 + x0, tmp73, 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)
        buf0 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_avg_pool2d_0[grid(64)](arg0_1, buf0, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del arg0_1
        buf1 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        buf2 = buf1
        del buf1
        buf3 = reinterpret_tensor(buf2, (4, 4, 1, 1), (4, 1, 1, 1), 0)
        del buf2
        triton_poi_fused_avg_pool2d_1[grid(16)](buf3, buf0, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del buf0
    return buf3,


class InputInjectionNew(nn.Module):
    """Downsampling module for CGNet."""

    def __init__(self, num_downsampling):
        super(InputInjectionNew, self).__init__()
        self.pool = nn.ModuleList()
        for i in range(num_downsampling):
            self.pool.append(nn.AvgPool2d(3, stride=2, padding=1))

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