# 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_7/inductor_cache/fr/cfrb645vkznmw7ooldvqafzhlfzyxdmsabwmfqp5bym34xyzoiuu.py
# Topologically Sorted Source Nodes: [origin_code, sum_1, abs_1, mean, trans_code, sum_2, abs_2, mean_1, add, code_balance_loss], Original ATen: [aten.sign, aten.sum, aten.abs, aten.mean, aten.add, aten.div]
# Source node to ATen node mapping:
#   abs_1 => abs_1
#   abs_2 => abs_2
#   add => add
#   code_balance_loss => div
#   mean => mean
#   mean_1 => mean_1
#   origin_code => sign
#   sum_1 => sum_1
#   sum_2 => sum_2
#   trans_code => sign_1
# Graph fragment:
#   %sign : [num_users=2] = call_function[target=torch.ops.aten.sign.default](args = (%arg0_1,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%sign, [1]), kwargs = {})
#   %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%sum_1,), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%abs_1,), kwargs = {})
#   %sign_1 : [num_users=2] = call_function[target=torch.ops.aten.sign.default](args = (%arg1_1,), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%sign_1, [1]), kwargs = {})
#   %abs_2 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%sum_2,), kwargs = {})
#   %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%abs_2,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mean, %mean_1), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add, 2), kwargs = {})
triton_per_fused_abs_add_div_mean_sign_sum_0 = async_compile.triton('triton_per_fused_abs_add_div_mean_sign_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.persistent_reduction(
    size_hints=[1, 64],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 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': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_abs_add_div_mean_sign_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 2, '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}
)
@triton.jit
def triton_per_fused_abs_add_div_mean_sign_sum_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex % 16
    r1 = (rindex // 16)
    tmp0 = tl.load(in_ptr0 + (r0 + (64*r1)), None)
    tmp8 = tl.load(in_ptr0 + (16 + r0 + (64*r1)), None)
    tmp16 = tl.load(in_ptr0 + (32 + r0 + (64*r1)), None)
    tmp24 = tl.load(in_ptr0 + (48 + r0 + (64*r1)), None)
    tmp36 = tl.load(in_ptr1 + (r0 + (64*r1)), None)
    tmp43 = tl.load(in_ptr1 + (16 + r0 + (64*r1)), None)
    tmp51 = tl.load(in_ptr1 + (32 + r0 + (64*r1)), None)
    tmp59 = tl.load(in_ptr1 + (48 + r0 + (64*r1)), None)
    tmp1 = tl.full([1, 1], 0, tl.int32)
    tmp2 = tmp1 < tmp0
    tmp3 = tmp2.to(tl.int8)
    tmp4 = tmp0 < tmp1
    tmp5 = tmp4.to(tl.int8)
    tmp6 = tmp3 - tmp5
    tmp7 = tmp6.to(tmp0.dtype)
    tmp9 = tmp1 < tmp8
    tmp10 = tmp9.to(tl.int8)
    tmp11 = tmp8 < tmp1
    tmp12 = tmp11.to(tl.int8)
    tmp13 = tmp10 - tmp12
    tmp14 = tmp13.to(tmp8.dtype)
    tmp15 = tmp7 + tmp14
    tmp17 = tmp1 < tmp16
    tmp18 = tmp17.to(tl.int8)
    tmp19 = tmp16 < tmp1
    tmp20 = tmp19.to(tl.int8)
    tmp21 = tmp18 - tmp20
    tmp22 = tmp21.to(tmp16.dtype)
    tmp23 = tmp15 + tmp22
    tmp25 = tmp1 < tmp24
    tmp26 = tmp25.to(tl.int8)
    tmp27 = tmp24 < tmp1
    tmp28 = tmp27.to(tl.int8)
    tmp29 = tmp26 - tmp28
    tmp30 = tmp29.to(tmp24.dtype)
    tmp31 = tmp23 + tmp30
    tmp32 = tl_math.abs(tmp31)
    tmp33 = tl.broadcast_to(tmp32, [XBLOCK, RBLOCK])
    tmp35 = tl.sum(tmp33, 1)[:, None]
    tmp37 = tmp1 < tmp36
    tmp38 = tmp37.to(tl.int8)
    tmp39 = tmp36 < tmp1
    tmp40 = tmp39.to(tl.int8)
    tmp41 = tmp38 - tmp40
    tmp42 = tmp41.to(tmp36.dtype)
    tmp44 = tmp1 < tmp43
    tmp45 = tmp44.to(tl.int8)
    tmp46 = tmp43 < tmp1
    tmp47 = tmp46.to(tl.int8)
    tmp48 = tmp45 - tmp47
    tmp49 = tmp48.to(tmp43.dtype)
    tmp50 = tmp42 + tmp49
    tmp52 = tmp1 < tmp51
    tmp53 = tmp52.to(tl.int8)
    tmp54 = tmp51 < tmp1
    tmp55 = tmp54.to(tl.int8)
    tmp56 = tmp53 - tmp55
    tmp57 = tmp56.to(tmp51.dtype)
    tmp58 = tmp50 + tmp57
    tmp60 = tmp1 < tmp59
    tmp61 = tmp60.to(tl.int8)
    tmp62 = tmp59 < tmp1
    tmp63 = tmp62.to(tl.int8)
    tmp64 = tmp61 - tmp63
    tmp65 = tmp64.to(tmp59.dtype)
    tmp66 = tmp58 + tmp65
    tmp67 = tl_math.abs(tmp66)
    tmp68 = tl.broadcast_to(tmp67, [XBLOCK, RBLOCK])
    tmp70 = tl.sum(tmp68, 1)[:, None]
    tmp71 = 64.0
    tmp72 = tmp35 / tmp71
    tmp73 = tmp70 / tmp71
    tmp74 = tmp72 + tmp73
    tmp75 = 0.5
    tmp76 = tmp74 * tmp75
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp76, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/el/celuuec6lck7idayrkstn2ksyecjnv6f76kxurv62g267qgyw5pr.py
# Topologically Sorted Source Nodes: [origin_code, trans_code, code_loss], Original ATen: [aten.sign, aten.mse_loss]
# Source node to ATen node mapping:
#   code_loss => mean_2, pow_1, sub
#   origin_code => sign
#   trans_code => sign_1
# Graph fragment:
#   %sign : [num_users=2] = call_function[target=torch.ops.aten.sign.default](args = (%arg0_1,), kwargs = {})
#   %sign_1 : [num_users=2] = call_function[target=torch.ops.aten.sign.default](args = (%arg1_1,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sign_1, %sign), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %mean_2 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%pow_1,), kwargs = {})
triton_per_fused_mse_loss_sign_1 = async_compile.triton('triton_per_fused_mse_loss_sign_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=[1, 256],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 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': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_mse_loss_sign_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 2, 'num_reduction': 1, '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}
)
@triton.jit
def triton_per_fused_mse_loss_sign_1(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel):
    xnumel = 1
    XBLOCK: tl.constexpr = 1
    rnumel = 256
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp8 = tl.load(in_ptr1 + (r0), None)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = tmp1 < tmp0
    tmp3 = tmp2.to(tl.int8)
    tmp4 = tmp0 < tmp1
    tmp5 = tmp4.to(tl.int8)
    tmp6 = tmp3 - tmp5
    tmp7 = tmp6.to(tmp0.dtype)
    tmp9 = tmp1 < tmp8
    tmp10 = tmp9.to(tl.int8)
    tmp11 = tmp8 < tmp1
    tmp12 = tmp11.to(tl.int8)
    tmp13 = tmp10 - tmp12
    tmp14 = tmp13.to(tmp8.dtype)
    tmp15 = tmp7 - tmp14
    tmp16 = tmp15 * tmp15
    tmp17 = tl.broadcast_to(tmp16, [RBLOCK])
    tmp19 = triton_helpers.promote_to_tensor(tl.sum(tmp17, 0))
    tmp20 = 256.0
    tmp21 = tmp19 / tmp20
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp21, None)
''', 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((), (), torch.float32)
        buf3 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [origin_code, sum_1, abs_1, mean, trans_code, sum_2, abs_2, mean_1, add, code_balance_loss], Original ATen: [aten.sign, aten.sum, aten.abs, aten.mean, aten.add, aten.div]
        stream0 = get_raw_stream(0)
        triton_per_fused_abs_add_div_mean_sign_sum_0.run(buf3, arg0_1, arg1_1, 1, 64, grid=grid(1), stream=stream0)
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [origin_code, trans_code, code_loss], Original ATen: [aten.sign, aten.mse_loss]
        triton_per_fused_mse_loss_sign_1.run(buf4, arg1_1, arg0_1, 1, 256, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
    return (buf3, buf4, )


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.nn as nn


class CodeLoss(nn.Module):

    def __init__(self):
        super().__init__()
        self.loss = nn.MSELoss()

    def forward(self, origin_logit, trans_logit):
        origin_code, trans_code = torch.sign(origin_logit), torch.sign(
            trans_logit)
        code_balance_loss = (torch.mean(torch.abs(torch.sum(origin_code,
            dim=1))) + torch.mean(torch.abs(torch.sum(trans_code, dim=1)))) / 2
        code_loss = self.loss(trans_code, origin_code.detach())
        return code_balance_loss, code_loss


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), torch.rand([4, 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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
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_per_fused_abs_add_div_mean_sign_sum_0(in_out_ptr0, in_ptr0,
    in_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex % 16
    r1 = rindex // 16
    tmp0 = tl.load(in_ptr0 + (r0 + 64 * r1), None)
    tmp8 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
    tmp16 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
    tmp24 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
    tmp36 = tl.load(in_ptr1 + (r0 + 64 * r1), None)
    tmp43 = tl.load(in_ptr1 + (16 + r0 + 64 * r1), None)
    tmp51 = tl.load(in_ptr1 + (32 + r0 + 64 * r1), None)
    tmp59 = tl.load(in_ptr1 + (48 + r0 + 64 * r1), None)
    tmp1 = tl.full([1, 1], 0, tl.int32)
    tmp2 = tmp1 < tmp0
    tmp3 = tmp2.to(tl.int8)
    tmp4 = tmp0 < tmp1
    tmp5 = tmp4.to(tl.int8)
    tmp6 = tmp3 - tmp5
    tmp7 = tmp6.to(tmp0.dtype)
    tmp9 = tmp1 < tmp8
    tmp10 = tmp9.to(tl.int8)
    tmp11 = tmp8 < tmp1
    tmp12 = tmp11.to(tl.int8)
    tmp13 = tmp10 - tmp12
    tmp14 = tmp13.to(tmp8.dtype)
    tmp15 = tmp7 + tmp14
    tmp17 = tmp1 < tmp16
    tmp18 = tmp17.to(tl.int8)
    tmp19 = tmp16 < tmp1
    tmp20 = tmp19.to(tl.int8)
    tmp21 = tmp18 - tmp20
    tmp22 = tmp21.to(tmp16.dtype)
    tmp23 = tmp15 + tmp22
    tmp25 = tmp1 < tmp24
    tmp26 = tmp25.to(tl.int8)
    tmp27 = tmp24 < tmp1
    tmp28 = tmp27.to(tl.int8)
    tmp29 = tmp26 - tmp28
    tmp30 = tmp29.to(tmp24.dtype)
    tmp31 = tmp23 + tmp30
    tmp32 = tl_math.abs(tmp31)
    tmp33 = tl.broadcast_to(tmp32, [XBLOCK, RBLOCK])
    tmp35 = tl.sum(tmp33, 1)[:, None]
    tmp37 = tmp1 < tmp36
    tmp38 = tmp37.to(tl.int8)
    tmp39 = tmp36 < tmp1
    tmp40 = tmp39.to(tl.int8)
    tmp41 = tmp38 - tmp40
    tmp42 = tmp41.to(tmp36.dtype)
    tmp44 = tmp1 < tmp43
    tmp45 = tmp44.to(tl.int8)
    tmp46 = tmp43 < tmp1
    tmp47 = tmp46.to(tl.int8)
    tmp48 = tmp45 - tmp47
    tmp49 = tmp48.to(tmp43.dtype)
    tmp50 = tmp42 + tmp49
    tmp52 = tmp1 < tmp51
    tmp53 = tmp52.to(tl.int8)
    tmp54 = tmp51 < tmp1
    tmp55 = tmp54.to(tl.int8)
    tmp56 = tmp53 - tmp55
    tmp57 = tmp56.to(tmp51.dtype)
    tmp58 = tmp50 + tmp57
    tmp60 = tmp1 < tmp59
    tmp61 = tmp60.to(tl.int8)
    tmp62 = tmp59 < tmp1
    tmp63 = tmp62.to(tl.int8)
    tmp64 = tmp61 - tmp63
    tmp65 = tmp64.to(tmp59.dtype)
    tmp66 = tmp58 + tmp65
    tmp67 = tl_math.abs(tmp66)
    tmp68 = tl.broadcast_to(tmp67, [XBLOCK, RBLOCK])
    tmp70 = tl.sum(tmp68, 1)[:, None]
    tmp71 = 64.0
    tmp72 = tmp35 / tmp71
    tmp73 = tmp70 / tmp71
    tmp74 = tmp72 + tmp73
    tmp75 = 0.5
    tmp76 = tmp74 * tmp75
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp76, None)


@triton.jit
def triton_per_fused_mse_loss_sign_1(in_out_ptr0, in_ptr0, in_ptr1, xnumel,
    rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp8 = tl.load(in_ptr1 + r0, None)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = tmp1 < tmp0
    tmp3 = tmp2.to(tl.int8)
    tmp4 = tmp0 < tmp1
    tmp5 = tmp4.to(tl.int8)
    tmp6 = tmp3 - tmp5
    tmp7 = tmp6.to(tmp0.dtype)
    tmp9 = tmp1 < tmp8
    tmp10 = tmp9.to(tl.int8)
    tmp11 = tmp8 < tmp1
    tmp12 = tmp11.to(tl.int8)
    tmp13 = tmp10 - tmp12
    tmp14 = tmp13.to(tmp8.dtype)
    tmp15 = tmp7 - tmp14
    tmp16 = tmp15 * tmp15
    tmp17 = tl.broadcast_to(tmp16, [RBLOCK])
    tmp19 = triton_helpers.promote_to_tensor(tl.sum(tmp17, 0))
    tmp20 = 256.0
    tmp21 = tmp19 / tmp20
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp21, None)


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((), (), torch.float32)
        buf3 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_abs_add_div_mean_sign_sum_0[grid(1)](buf3, arg0_1,
            arg1_1, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf2
        del buf2
        triton_per_fused_mse_loss_sign_1[grid(1)](buf4, arg1_1, arg0_1, 1, 
            256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf3, buf4


class CodeLossNew(nn.Module):

    def __init__(self):
        super().__init__()
        self.loss = nn.MSELoss()

    def forward(self, input_0, input_1):
        arg0_1 = input_0
        arg1_1 = input_1
        output = call([arg0_1, arg1_1])
        return output[0], output[1]
