# 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/i2/ci2anxc6j3onqz7lsepmpntcefsw2ad3zwn2zieyso27x4ese723.py
# Topologically Sorted Source Nodes: [sub, L, L_1, L_2], Original ATen: [aten.sub, aten.pow, aten.sum]
# Source node to ATen node mapping:
#   L => pow_1
#   L_1 => sum_1
#   L_2 => sum_2
#   sub => sub
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %arg1_1), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [-1]), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%sum_1, [-1]), kwargs = {})
triton_poi_fused_pow_sub_sum_0 = async_compile.triton('triton_poi_fused_pow_sub_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.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 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, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_pow_sub_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 32, '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_pow_sub_sum_0(in_ptr0, in_ptr1, 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 + (16*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (16*x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (1 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (1 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (2 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (2 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr0 + (3 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr1 + (3 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr0 + (4 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr1 + (4 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr0 + (5 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp24 = tl.load(in_ptr1 + (5 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr0 + (6 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp29 = tl.load(in_ptr1 + (6 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp33 = tl.load(in_ptr0 + (7 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp34 = tl.load(in_ptr1 + (7 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp39 = tl.load(in_ptr0 + (8 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp40 = tl.load(in_ptr1 + (8 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp43 = tl.load(in_ptr0 + (9 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp44 = tl.load(in_ptr1 + (9 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp48 = tl.load(in_ptr0 + (10 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp49 = tl.load(in_ptr1 + (10 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp53 = tl.load(in_ptr0 + (11 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp54 = tl.load(in_ptr1 + (11 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp59 = tl.load(in_ptr0 + (12 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp60 = tl.load(in_ptr1 + (12 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp63 = tl.load(in_ptr0 + (13 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp64 = tl.load(in_ptr1 + (13 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp68 = tl.load(in_ptr0 + (14 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp69 = tl.load(in_ptr1 + (14 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp73 = tl.load(in_ptr0 + (15 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp74 = tl.load(in_ptr1 + (15 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp6 = tmp4 - tmp5
    tmp7 = tmp6 * tmp6
    tmp8 = tmp3 + tmp7
    tmp11 = tmp9 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tmp8 + tmp12
    tmp16 = tmp14 - tmp15
    tmp17 = tmp16 * tmp16
    tmp18 = tmp13 + tmp17
    tmp21 = tmp19 - tmp20
    tmp22 = tmp21 * tmp21
    tmp25 = tmp23 - tmp24
    tmp26 = tmp25 * tmp25
    tmp27 = tmp22 + tmp26
    tmp30 = tmp28 - tmp29
    tmp31 = tmp30 * tmp30
    tmp32 = tmp27 + tmp31
    tmp35 = tmp33 - tmp34
    tmp36 = tmp35 * tmp35
    tmp37 = tmp32 + tmp36
    tmp38 = tmp18 + tmp37
    tmp41 = tmp39 - tmp40
    tmp42 = tmp41 * tmp41
    tmp45 = tmp43 - tmp44
    tmp46 = tmp45 * tmp45
    tmp47 = tmp42 + tmp46
    tmp50 = tmp48 - tmp49
    tmp51 = tmp50 * tmp50
    tmp52 = tmp47 + tmp51
    tmp55 = tmp53 - tmp54
    tmp56 = tmp55 * tmp55
    tmp57 = tmp52 + tmp56
    tmp58 = tmp38 + tmp57
    tmp61 = tmp59 - tmp60
    tmp62 = tmp61 * tmp61
    tmp65 = tmp63 - tmp64
    tmp66 = tmp65 * tmp65
    tmp67 = tmp62 + tmp66
    tmp70 = tmp68 - tmp69
    tmp71 = tmp70 * tmp70
    tmp72 = tmp67 + tmp71
    tmp75 = tmp73 - tmp74
    tmp76 = tmp75 * tmp75
    tmp77 = tmp72 + tmp76
    tmp78 = tmp58 + tmp77
    tl.store(out_ptr0 + (x0), tmp78, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/2i/c2ingmbimtcrt54hrpybw6y7th4d4urmdjnaajzzxipsjgfg3wo3.py
# Topologically Sorted Source Nodes: [L_3, mean], Original ATen: [aten.sum, aten.mean]
# Source node to ATen node mapping:
#   L_3 => sum_3
#   mean => mean
# Graph fragment:
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%sum_2, [-1]), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sum_3,), kwargs = {})
triton_per_fused_mean_sum_1 = async_compile.triton('triton_per_fused_mean_sum_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, 4],
    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': {2: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=(2,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_mean_sum_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, '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_mean_sum_1(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 4
    RBLOCK: tl.constexpr = 4
    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
    tmp0 = tl.load(in_ptr0 + (4*r0), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (4*r0)), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + (4*r0)), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + (4*r0)), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp9 = tl.sum(tmp7, 1)[:, None]
    tmp10 = 4.0
    tmp11 = tmp9 / tmp10
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp11, 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((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sub, L, L_1, L_2], Original ATen: [aten.sub, aten.pow, aten.sum]
        stream0 = get_raw_stream(0)
        triton_poi_fused_pow_sub_sum_0.run(arg0_1, arg1_1, buf0, 16, grid=grid(16), stream=stream0)
        del arg0_1
        del arg1_1
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [L_3, mean], Original ATen: [aten.sum, aten.mean]
        triton_per_fused_mean_sum_1.run(buf2, buf0, 1, 4, grid=grid(1), stream=stream0)
        del buf0
    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((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
from functools import reduce
import torch.nn as nn


class BaseModule(nn.Module):
    """
    Implements the basic module.
    All other modules inherit from this one
    """

    def load_w(self, checkpoint_path):
        """
        Loads a checkpoint into the state_dict.

        :param checkpoint_path: the checkpoint file to be loaded.
        """
        device = torch.device('cuda:' + '1')
        self.load_state_dict(torch.load(checkpoint_path, map_location=device))

    def __repr__(self):
        """
        String representation
        """
        good_old = super(BaseModule, self).__repr__()
        addition = 'Total number of parameters: {:,}'.format(self.n_parameters)
        return good_old + '\n' + addition

    def __call__(self, *args, **kwargs):
        return super(BaseModule, self).__call__(*args, **kwargs)

    @property
    def n_parameters(self):
        """
        Number of parameters of the model.
        """
        n_parameters = 0
        for p in self.parameters():
            if hasattr(p, 'mask'):
                n_parameters += torch.sum(p.mask).item()
            else:
                n_parameters += reduce(mul, p.shape)
        return int(n_parameters)


class ReconstructionLoss(BaseModule):
    """
    Implements the reconstruction loss.
    """

    def __init__(self):
        """
        Class constructor.
        """
        super(ReconstructionLoss, self).__init__()

    def forward(self, x, x_r):
        """
        Forward propagation.

        :param x: the batch of input samples.
        :param x_r: the batch of reconstructions.
        :return: the mean reconstruction loss (averaged along the batch axis).
        """
        L = torch.pow(x - x_r, 2)
        while L.dim() > 1:
            L = torch.sum(L, dim=-1)
        return torch.mean(L)


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 functools import reduce
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_pow_sub_sum_0(in_ptr0, in_ptr1, 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 + 16 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 16 * x0, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (1 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp5 = tl.load(in_ptr1 + (1 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp9 = tl.load(in_ptr0 + (2 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp10 = tl.load(in_ptr1 + (2 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp14 = tl.load(in_ptr0 + (3 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp15 = tl.load(in_ptr1 + (3 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp19 = tl.load(in_ptr0 + (4 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp20 = tl.load(in_ptr1 + (4 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp23 = tl.load(in_ptr0 + (5 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp24 = tl.load(in_ptr1 + (5 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp28 = tl.load(in_ptr0 + (6 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp29 = tl.load(in_ptr1 + (6 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp33 = tl.load(in_ptr0 + (7 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp34 = tl.load(in_ptr1 + (7 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp39 = tl.load(in_ptr0 + (8 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp40 = tl.load(in_ptr1 + (8 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp43 = tl.load(in_ptr0 + (9 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp44 = tl.load(in_ptr1 + (9 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp48 = tl.load(in_ptr0 + (10 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp49 = tl.load(in_ptr1 + (10 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp53 = tl.load(in_ptr0 + (11 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp54 = tl.load(in_ptr1 + (11 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp59 = tl.load(in_ptr0 + (12 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp60 = tl.load(in_ptr1 + (12 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp63 = tl.load(in_ptr0 + (13 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp64 = tl.load(in_ptr1 + (13 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp68 = tl.load(in_ptr0 + (14 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp69 = tl.load(in_ptr1 + (14 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp73 = tl.load(in_ptr0 + (15 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp74 = tl.load(in_ptr1 + (15 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp6 = tmp4 - tmp5
    tmp7 = tmp6 * tmp6
    tmp8 = tmp3 + tmp7
    tmp11 = tmp9 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tmp8 + tmp12
    tmp16 = tmp14 - tmp15
    tmp17 = tmp16 * tmp16
    tmp18 = tmp13 + tmp17
    tmp21 = tmp19 - tmp20
    tmp22 = tmp21 * tmp21
    tmp25 = tmp23 - tmp24
    tmp26 = tmp25 * tmp25
    tmp27 = tmp22 + tmp26
    tmp30 = tmp28 - tmp29
    tmp31 = tmp30 * tmp30
    tmp32 = tmp27 + tmp31
    tmp35 = tmp33 - tmp34
    tmp36 = tmp35 * tmp35
    tmp37 = tmp32 + tmp36
    tmp38 = tmp18 + tmp37
    tmp41 = tmp39 - tmp40
    tmp42 = tmp41 * tmp41
    tmp45 = tmp43 - tmp44
    tmp46 = tmp45 * tmp45
    tmp47 = tmp42 + tmp46
    tmp50 = tmp48 - tmp49
    tmp51 = tmp50 * tmp50
    tmp52 = tmp47 + tmp51
    tmp55 = tmp53 - tmp54
    tmp56 = tmp55 * tmp55
    tmp57 = tmp52 + tmp56
    tmp58 = tmp38 + tmp57
    tmp61 = tmp59 - tmp60
    tmp62 = tmp61 * tmp61
    tmp65 = tmp63 - tmp64
    tmp66 = tmp65 * tmp65
    tmp67 = tmp62 + tmp66
    tmp70 = tmp68 - tmp69
    tmp71 = tmp70 * tmp70
    tmp72 = tmp67 + tmp71
    tmp75 = tmp73 - tmp74
    tmp76 = tmp75 * tmp75
    tmp77 = tmp72 + tmp76
    tmp78 = tmp58 + tmp77
    tl.store(out_ptr0 + x0, tmp78, xmask)


@triton.jit
def triton_per_fused_mean_sum_1(in_out_ptr0, in_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 4
    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
    tmp0 = tl.load(in_ptr0 + 4 * r0, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp9 = tl.sum(tmp7, 1)[:, None]
    tmp10 = 4.0
    tmp11 = tmp9 / tmp10
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp11, 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((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_pow_sub_sum_0[grid(16)](arg0_1, arg1_1, buf0, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del arg0_1
        del arg1_1
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1
        del buf1
        triton_per_fused_mean_sum_1[grid(1)](buf2, buf0, 1, 4, XBLOCK=1,
            num_warps=2, num_stages=1)
        del buf0
    return buf2,


class BaseModule(nn.Module):
    """
    Implements the basic module.
    All other modules inherit from this one
    """

    def load_w(self, checkpoint_path):
        """
        Loads a checkpoint into the state_dict.

        :param checkpoint_path: the checkpoint file to be loaded.
        """
        device = torch.device('cuda:' + '1')
        self.load_state_dict(torch.load(checkpoint_path, map_location=device))

    def __repr__(self):
        """
        String representation
        """
        good_old = super(BaseModule, self).__repr__()
        addition = 'Total number of parameters: {:,}'.format(self.n_parameters)
        return good_old + '\n' + addition

    def __call__(self, *args, **kwargs):
        return super(BaseModule, self).__call__(*args, **kwargs)

    @property
    def n_parameters(self):
        """
        Number of parameters of the model.
        """
        n_parameters = 0
        for p in self.parameters():
            if hasattr(p, 'mask'):
                n_parameters += torch.sum(p.mask).item()
            else:
                n_parameters += reduce(mul, p.shape)
        return int(n_parameters)


class ReconstructionLossNew(BaseModule):
    """
    Implements the reconstruction loss.
    """

    def __init__(self):
        """
        Class constructor.
        """
        super(ReconstructionLossNew, self).__init__()

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