# 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_4/inductor_cache/35/c35fcijpopaylf5zr7vinz4ztnvh6fbtbfsa76rp4ceoc6wqqxr4.py
# Topologically Sorted Source Nodes: [a, add, mul_3, a_1, add_2, mul_4, a_2, add_4, mul_5, a_3, mul_6, add_6, mul_7, a_4, b, add_7, mul_8, b_1, add_9, mul_9, b_2, add_11, mul_10, b_3, mul_11, add_13, mul_12, truediv_1, mul_13, add_14, mul_14, b_4, add_24, c, add_15, mul_15, c_1, add_17, mul_16, c_2, add_19, mul_17, c_3, mul_18, add_21, mul_19, truediv_3, mul_20, add_22, mul_21, truediv_4, mul_22, add_23, mul_23, c_4, add_25, pred], Original ATen: [aten.mul, aten.add, aten.div]
# Source node to ATen node mapping:
#   a => mul
#   a_1 => add_1
#   a_2 => add_3
#   a_3 => add_5
#   a_4 => div
#   add => add
#   add_11 => add_11
#   add_13 => add_13
#   add_14 => add_14
#   add_15 => add_15
#   add_17 => add_17
#   add_19 => add_19
#   add_2 => add_2
#   add_21 => add_21
#   add_22 => add_22
#   add_23 => add_23
#   add_24 => add_24
#   add_25 => add_25
#   add_4 => add_4
#   add_6 => add_6
#   add_7 => add_7
#   add_9 => add_9
#   b => mul_1
#   b_1 => add_8
#   b_2 => add_10
#   b_3 => add_12
#   b_4 => div_2
#   c => mul_2
#   c_1 => add_16
#   c_2 => add_18
#   c_3 => add_20
#   c_4 => div_5
#   mul_10 => mul_10
#   mul_11 => mul_11
#   mul_12 => mul_12
#   mul_13 => mul_13
#   mul_14 => mul_14
#   mul_15 => mul_15
#   mul_16 => mul_16
#   mul_17 => mul_17
#   mul_18 => mul_18
#   mul_19 => mul_19
#   mul_20 => mul_20
#   mul_21 => mul_21
#   mul_22 => mul_22
#   mul_23 => mul_23
#   mul_3 => mul_3
#   mul_4 => mul_4
#   mul_5 => mul_5
#   mul_6 => mul_6
#   mul_7 => mul_7
#   mul_8 => mul_8
#   mul_9 => mul_9
#   pred => mul_24
#   truediv_1 => div_1
#   truediv_3 => div_3
#   truediv_4 => div_4
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select_1, 0), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%select_6, -1), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, %select_8), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_3), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%select_9, 0), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_2, %select_11), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_1, %mul_4), kwargs = {})
#   %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%select_12, 1), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_4, %select_14), kwargs = {})
#   %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_3, %mul_5), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select_15, 1), kwargs = {})
#   %add_6 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_6, 1), kwargs = {})
#   %mul_7 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_6, 3), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add_5, %mul_7), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select_3, 0), kwargs = {})
#   %add_7 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%select_16, -1), kwargs = {})
#   %mul_8 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_7, %select_18), kwargs = {})
#   %add_8 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %mul_8), kwargs = {})
#   %add_9 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%select_19, 0), kwargs = {})
#   %mul_9 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_9, %select_21), kwargs = {})
#   %add_10 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_8, %mul_9), kwargs = {})
#   %add_11 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%select_22, 1), kwargs = {})
#   %mul_10 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_11, %select_24), kwargs = {})
#   %add_12 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_10, %mul_10), kwargs = {})
#   %mul_11 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select_25, 1), kwargs = {})
#   %add_13 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_11, 1), kwargs = {})
#   %mul_12 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_13, 3), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add_12, %mul_12), kwargs = {})
#   %mul_13 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select_26, 1), kwargs = {})
#   %add_14 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_13, 1), kwargs = {})
#   %mul_14 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_14, 3), kwargs = {})
#   %div_2 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%div_1, %mul_14), kwargs = {})
#   %add_24 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%div, %div_2), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select_5, 0), kwargs = {})
#   %add_15 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%select_27, -1), kwargs = {})
#   %mul_15 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_15, %select_29), kwargs = {})
#   %add_16 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_2, %mul_15), kwargs = {})
#   %add_17 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%select_30, 0), kwargs = {})
#   %mul_16 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_17, %select_32), kwargs = {})
#   %add_18 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_16, %mul_16), kwargs = {})
#   %add_19 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%select_33, 1), kwargs = {})
#   %mul_17 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_19, %select_35), kwargs = {})
#   %add_20 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_18, %mul_17), kwargs = {})
#   %mul_18 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select_36, 1), kwargs = {})
#   %add_21 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_18, 1), kwargs = {})
#   %mul_19 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_21, 3), kwargs = {})
#   %div_3 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add_20, %mul_19), kwargs = {})
#   %mul_20 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select_37, 1), kwargs = {})
#   %add_22 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_20, 1), kwargs = {})
#   %mul_21 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_22, 3), kwargs = {})
#   %div_4 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%div_3, %mul_21), kwargs = {})
#   %mul_22 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select_38, 1), kwargs = {})
#   %add_23 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_22, 1), kwargs = {})
#   %mul_23 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_23, 3), kwargs = {})
#   %div_5 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%div_4, %mul_23), kwargs = {})
#   %add_25 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_24, %div_5), kwargs = {})
#   %mul_24 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_25, 99), kwargs = {})
triton_poi_fused_add_div_mul_0 = async_compile.triton('triton_poi_fused_add_div_mul_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=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_mul_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 15, '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_mul_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (384 + x2), xmask)
    tmp9 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr0 + (192 + x2), xmask)
    tmp23 = tl.load(in_ptr0 + (448 + x2), xmask)
    tmp25 = tl.load(in_ptr0 + (64 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp29 = tl.load(in_ptr0 + (65 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp33 = tl.load(in_ptr0 + (66 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp37 = tl.load(in_ptr0 + (256 + x2), xmask)
    tmp43 = tl.load(in_ptr0 + (512 + x2), xmask)
    tmp45 = tl.load(in_ptr0 + (128 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp49 = tl.load(in_ptr0 + (129 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp53 = tl.load(in_ptr0 + (130 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp58 = tl.load(in_ptr0 + (320 + x2), xmask)
    tmp1 = 0.0
    tmp2 = tmp0 * tmp1
    tmp4 = -1.0
    tmp5 = tmp3 + tmp4
    tmp6 = tmp5 * tmp0
    tmp7 = tmp2 + tmp6
    tmp8 = tmp3 + tmp1
    tmp10 = tmp8 * tmp9
    tmp11 = tmp7 + tmp10
    tmp12 = 1.0
    tmp13 = tmp3 + tmp12
    tmp15 = tmp13 * tmp14
    tmp16 = tmp11 + tmp15
    tmp18 = tmp17 * tmp12
    tmp19 = tmp18 + tmp12
    tmp20 = 3.0
    tmp21 = tmp19 * tmp20
    tmp22 = tmp16 / tmp21
    tmp24 = tmp23 + tmp4
    tmp26 = tmp24 * tmp25
    tmp27 = tmp2 + tmp26
    tmp28 = tmp23 + tmp1
    tmp30 = tmp28 * tmp29
    tmp31 = tmp27 + tmp30
    tmp32 = tmp23 + tmp12
    tmp34 = tmp32 * tmp33
    tmp35 = tmp31 + tmp34
    tmp36 = tmp35 / tmp21
    tmp38 = tmp37 * tmp12
    tmp39 = tmp38 + tmp12
    tmp40 = tmp39 * tmp20
    tmp41 = tmp36 / tmp40
    tmp42 = tmp22 + tmp41
    tmp44 = tmp43 + tmp4
    tmp46 = tmp44 * tmp45
    tmp47 = tmp2 + tmp46
    tmp48 = tmp43 + tmp1
    tmp50 = tmp48 * tmp49
    tmp51 = tmp47 + tmp50
    tmp52 = tmp43 + tmp12
    tmp54 = tmp52 * tmp53
    tmp55 = tmp51 + tmp54
    tmp56 = tmp55 / tmp21
    tmp57 = tmp56 / tmp40
    tmp59 = tmp58 * tmp12
    tmp60 = tmp59 + tmp12
    tmp61 = tmp60 * tmp20
    tmp62 = tmp57 / tmp61
    tmp63 = tmp42 + tmp62
    tmp64 = 99.0
    tmp65 = tmp63 * tmp64
    tl.store(in_out_ptr0 + (x2), tmp65, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (9, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf2 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [a, add, mul_3, a_1, add_2, mul_4, a_2, add_4, mul_5, a_3, mul_6, add_6, mul_7, a_4, b, add_7, mul_8, b_1, add_9, mul_9, b_2, add_11, mul_10, b_3, mul_11, add_13, mul_12, truediv_1, mul_13, add_14, mul_14, b_4, add_24, c, add_15, mul_15, c_1, add_17, mul_16, c_2, add_19, mul_17, c_3, mul_18, add_21, mul_19, truediv_3, mul_20, add_22, mul_21, truediv_4, mul_22, add_23, mul_23, c_4, add_25, pred], Original ATen: [aten.mul, aten.add, aten.div]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_div_mul_0.run(buf2, arg0_1, 64, grid=grid(64), stream=stream0)
        del arg0_1
    return (buf2, )


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((9, 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


class SSRLayer(nn.Module):

    def __init__(self):
        super(SSRLayer, self).__init__()

    def forward(self, x):
        a = x[0][:, :, 0] * 0
        b = x[0][:, :, 0] * 0
        c = x[0][:, :, 0] * 0
        s1 = 3
        s2 = 3
        s3 = 3
        lambda_d = 1
        di = s1 // 2
        dj = s2 // 2
        dk = s3 // 2
        V = 99
        for i in range(0, s1):
            a = a + (i - di + x[6]) * x[0][:, :, i]
        a = a / (s1 * (1 + lambda_d * x[3]))
        for j in range(0, s2):
            b = b + (j - dj + x[7]) * x[1][:, :, j]
        b = b / (s1 * (1 + lambda_d * x[3])) / (s2 * (1 + lambda_d * x[4]))
        for k in range(0, s3):
            c = c + (k - dk + x[8]) * x[2][:, :, k]
        c = c / (s1 * (1 + lambda_d * x[3])) / (s2 * (1 + lambda_d * x[4])) / (
            s3 * (1 + lambda_d * x[5]))
        pred = (a + b + c) * V
        return pred


def get_inputs():
    return [torch.rand([9, 4, 4, 4])]


def get_init_inputs():
    return [[], {}]

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
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_div_mul_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (384 + x2), xmask)
    tmp9 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp17 = tl.load(in_ptr0 + (192 + x2), xmask)
    tmp23 = tl.load(in_ptr0 + (448 + x2), xmask)
    tmp25 = tl.load(in_ptr0 + (64 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp29 = tl.load(in_ptr0 + (65 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp33 = tl.load(in_ptr0 + (66 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp37 = tl.load(in_ptr0 + (256 + x2), xmask)
    tmp43 = tl.load(in_ptr0 + (512 + x2), xmask)
    tmp45 = tl.load(in_ptr0 + (128 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp49 = tl.load(in_ptr0 + (129 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp53 = tl.load(in_ptr0 + (130 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp58 = tl.load(in_ptr0 + (320 + x2), xmask)
    tmp1 = 0.0
    tmp2 = tmp0 * tmp1
    tmp4 = -1.0
    tmp5 = tmp3 + tmp4
    tmp6 = tmp5 * tmp0
    tmp7 = tmp2 + tmp6
    tmp8 = tmp3 + tmp1
    tmp10 = tmp8 * tmp9
    tmp11 = tmp7 + tmp10
    tmp12 = 1.0
    tmp13 = tmp3 + tmp12
    tmp15 = tmp13 * tmp14
    tmp16 = tmp11 + tmp15
    tmp18 = tmp17 * tmp12
    tmp19 = tmp18 + tmp12
    tmp20 = 3.0
    tmp21 = tmp19 * tmp20
    tmp22 = tmp16 / tmp21
    tmp24 = tmp23 + tmp4
    tmp26 = tmp24 * tmp25
    tmp27 = tmp2 + tmp26
    tmp28 = tmp23 + tmp1
    tmp30 = tmp28 * tmp29
    tmp31 = tmp27 + tmp30
    tmp32 = tmp23 + tmp12
    tmp34 = tmp32 * tmp33
    tmp35 = tmp31 + tmp34
    tmp36 = tmp35 / tmp21
    tmp38 = tmp37 * tmp12
    tmp39 = tmp38 + tmp12
    tmp40 = tmp39 * tmp20
    tmp41 = tmp36 / tmp40
    tmp42 = tmp22 + tmp41
    tmp44 = tmp43 + tmp4
    tmp46 = tmp44 * tmp45
    tmp47 = tmp2 + tmp46
    tmp48 = tmp43 + tmp1
    tmp50 = tmp48 * tmp49
    tmp51 = tmp47 + tmp50
    tmp52 = tmp43 + tmp12
    tmp54 = tmp52 * tmp53
    tmp55 = tmp51 + tmp54
    tmp56 = tmp55 / tmp21
    tmp57 = tmp56 / tmp40
    tmp59 = tmp58 * tmp12
    tmp60 = tmp59 + tmp12
    tmp61 = tmp60 * tmp20
    tmp62 = tmp57 / tmp61
    tmp63 = tmp42 + tmp62
    tmp64 = 99.0
    tmp65 = tmp63 * tmp64
    tl.store(in_out_ptr0 + x2, tmp65, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (9, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf2 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_add_div_mul_0[grid(64)](buf2, arg0_1, 64, XBLOCK=
            64, num_warps=1, num_stages=1)
        del arg0_1
    return buf2,


class SSRLayerNew(nn.Module):

    def __init__(self):
        super(SSRLayerNew, self).__init__()

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