# 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/ne/cnebqb4ivsqst5cjtek74fvg4bdcz5zjpfgrstqmpakikmxoy5wi.py
# Topologically Sorted Source Nodes: [binary_cross_entropy, loss, binary_cross_entropy_1, loss_1, binary_cross_entropy_2, loss_2, binary_cross_entropy_3, loss_3, truediv], Original ATen: [aten.binary_cross_entropy, aten.add, aten.div]
# Source node to ATen node mapping:
#   binary_cross_entropy => full_default, full_default_1, log, log1p, maximum, maximum_1, mean, mul, mul_1, neg, sub, sub_1
#   binary_cross_entropy_1 => full_default_2, full_default_3, log1p_1, log_1, maximum_2, maximum_3, mean_1, mul_2, mul_3, neg_1, sub_2, sub_3
#   binary_cross_entropy_2 => full_default_4, full_default_5, log1p_2, log_2, maximum_4, maximum_5, mean_2, mul_4, mul_5, neg_2, sub_4, sub_5
#   binary_cross_entropy_3 => full_default_6, full_default_7, log1p_3, log_3, maximum_6, maximum_7, mean_3, mul_6, mul_7, neg_3, sub_6, sub_7
#   loss => add
#   loss_1 => add_1
#   loss_2 => add_2
#   loss_3 => add_3
#   truediv => div
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%select_1, 1), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%select,), kwargs = {})
#   %log1p : [num_users=1] = call_function[target=torch.ops.aten.log1p.default](args = (%neg,), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], -100), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %maximum : [num_users=1] = call_function[target=torch.ops.aten.maximum.default](args = (%log1p, %full_default), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %maximum), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%select,), kwargs = {})
#   %full_default_1 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], -100), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %maximum_1 : [num_users=1] = call_function[target=torch.ops.aten.maximum.default](args = (%log, %full_default_1), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select_1, %maximum_1), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul, %mul_1), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sub_1,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mean, 0), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%select_3, 1), kwargs = {})
#   %neg_1 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%select_2,), kwargs = {})
#   %log1p_1 : [num_users=1] = call_function[target=torch.ops.aten.log1p.default](args = (%neg_1,), kwargs = {})
#   %full_default_2 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], -100), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %maximum_2 : [num_users=1] = call_function[target=torch.ops.aten.maximum.default](args = (%log1p_1, %full_default_2), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_2, %maximum_2), kwargs = {})
#   %log_1 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%select_2,), kwargs = {})
#   %full_default_3 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], -100), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %maximum_3 : [num_users=1] = call_function[target=torch.ops.aten.maximum.default](args = (%log_1, %full_default_3), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select_3, %maximum_3), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_2, %mul_3), kwargs = {})
#   %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sub_3,), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add, %mean_1), kwargs = {})
#   %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%select_5, 1), kwargs = {})
#   %neg_2 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%select_4,), kwargs = {})
#   %log1p_2 : [num_users=1] = call_function[target=torch.ops.aten.log1p.default](args = (%neg_2,), kwargs = {})
#   %full_default_4 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], -100), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %maximum_4 : [num_users=1] = call_function[target=torch.ops.aten.maximum.default](args = (%log1p_2, %full_default_4), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_4, %maximum_4), kwargs = {})
#   %log_2 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%select_4,), kwargs = {})
#   %full_default_5 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], -100), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %maximum_5 : [num_users=1] = call_function[target=torch.ops.aten.maximum.default](args = (%log_2, %full_default_5), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select_5, %maximum_5), kwargs = {})
#   %sub_5 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_4, %mul_5), kwargs = {})
#   %mean_2 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sub_5,), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_1, %mean_2), kwargs = {})
#   %sub_6 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%select_7, 1), kwargs = {})
#   %neg_3 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%select_6,), kwargs = {})
#   %log1p_3 : [num_users=1] = call_function[target=torch.ops.aten.log1p.default](args = (%neg_3,), kwargs = {})
#   %full_default_6 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], -100), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %maximum_6 : [num_users=1] = call_function[target=torch.ops.aten.maximum.default](args = (%log1p_3, %full_default_6), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_6, %maximum_6), kwargs = {})
#   %log_3 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%select_6,), kwargs = {})
#   %full_default_7 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], -100), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %maximum_7 : [num_users=1] = call_function[target=torch.ops.aten.maximum.default](args = (%log_3, %full_default_7), kwargs = {})
#   %mul_7 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select_7, %maximum_7), kwargs = {})
#   %sub_7 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_6, %mul_7), kwargs = {})
#   %mean_3 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sub_7,), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_2, %mean_3), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add_3, 4), kwargs = {})
triton_per_fused_add_binary_cross_entropy_div_0 = async_compile.triton('triton_per_fused_add_binary_cross_entropy_div_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_add_binary_cross_entropy_div_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 4, '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_add_binary_cross_entropy_div_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)
    tmp3 = tl.load(in_ptr1 + (r0 + (64*r1)), None)
    tmp16 = tl.load(in_ptr0 + (16 + r0 + (64*r1)), None)
    tmp18 = tl.load(in_ptr1 + (16 + r0 + (64*r1)), None)
    tmp30 = tl.load(in_ptr0 + (32 + r0 + (64*r1)), None)
    tmp32 = tl.load(in_ptr1 + (32 + r0 + (64*r1)), None)
    tmp44 = tl.load(in_ptr0 + (48 + r0 + (64*r1)), None)
    tmp46 = tl.load(in_ptr1 + (48 + r0 + (64*r1)), None)
    tmp1 = 1.0
    tmp2 = tmp0 - tmp1
    tmp4 = -tmp3
    tmp5 = libdevice.log1p(tmp4)
    tmp6 = -100.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp2 * tmp7
    tmp9 = tl_math.log(tmp3)
    tmp10 = triton_helpers.maximum(tmp9, tmp6)
    tmp11 = tmp0 * tmp10
    tmp12 = tmp8 - tmp11
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
    tmp15 = tl.sum(tmp13, 1)[:, None]
    tmp17 = tmp16 - tmp1
    tmp19 = -tmp18
    tmp20 = libdevice.log1p(tmp19)
    tmp21 = triton_helpers.maximum(tmp20, tmp6)
    tmp22 = tmp17 * tmp21
    tmp23 = tl_math.log(tmp18)
    tmp24 = triton_helpers.maximum(tmp23, tmp6)
    tmp25 = tmp16 * tmp24
    tmp26 = tmp22 - tmp25
    tmp27 = tl.broadcast_to(tmp26, [XBLOCK, RBLOCK])
    tmp29 = tl.sum(tmp27, 1)[:, None]
    tmp31 = tmp30 - tmp1
    tmp33 = -tmp32
    tmp34 = libdevice.log1p(tmp33)
    tmp35 = triton_helpers.maximum(tmp34, tmp6)
    tmp36 = tmp31 * tmp35
    tmp37 = tl_math.log(tmp32)
    tmp38 = triton_helpers.maximum(tmp37, tmp6)
    tmp39 = tmp30 * tmp38
    tmp40 = tmp36 - tmp39
    tmp41 = tl.broadcast_to(tmp40, [XBLOCK, RBLOCK])
    tmp43 = tl.sum(tmp41, 1)[:, None]
    tmp45 = tmp44 - tmp1
    tmp47 = -tmp46
    tmp48 = libdevice.log1p(tmp47)
    tmp49 = triton_helpers.maximum(tmp48, tmp6)
    tmp50 = tmp45 * tmp49
    tmp51 = tl_math.log(tmp46)
    tmp52 = triton_helpers.maximum(tmp51, tmp6)
    tmp53 = tmp44 * tmp52
    tmp54 = tmp50 - tmp53
    tmp55 = tl.broadcast_to(tmp54, [XBLOCK, RBLOCK])
    tmp57 = tl.sum(tmp55, 1)[:, None]
    tmp58 = 64.0
    tmp59 = tmp15 / tmp58
    tmp60 = 0.0
    tmp61 = tmp59 + tmp60
    tmp62 = tmp29 / tmp58
    tmp63 = tmp61 + tmp62
    tmp64 = tmp43 / tmp58
    tmp65 = tmp63 + tmp64
    tmp66 = tmp57 / tmp58
    tmp67 = tmp65 + tmp66
    tmp68 = 0.25
    tmp69 = tmp67 * tmp68
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp69, 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)
        buf4 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [binary_cross_entropy, loss, binary_cross_entropy_1, loss_1, binary_cross_entropy_2, loss_2, binary_cross_entropy_3, loss_3, truediv], Original ATen: [aten.binary_cross_entropy, aten.add, aten.div]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_binary_cross_entropy_div_0.run(buf4, arg1_1, arg0_1, 1, 64, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
    return (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
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed


class GenerativeLoss(nn.Module):

    def __init__(self):
        super(GenerativeLoss, self).__init__()
        self.criterion = nn.BCELoss(reduction='mean')

    def forward(self, output, target):
        num_joints = output.shape[1]
        loss = 0
        for idx in range(num_joints):
            real_or_fake_pred = output[:, idx]
            real_or_fake_gt = target[:, idx]
            loss += self.criterion(real_or_fake_pred, real_or_fake_gt)
        return loss / num_joints


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 libdevice, math as tl_math
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
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_add_binary_cross_entropy_div_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)
    tmp3 = tl.load(in_ptr1 + (r0 + 64 * r1), None)
    tmp16 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
    tmp18 = tl.load(in_ptr1 + (16 + r0 + 64 * r1), None)
    tmp30 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
    tmp32 = tl.load(in_ptr1 + (32 + r0 + 64 * r1), None)
    tmp44 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
    tmp46 = tl.load(in_ptr1 + (48 + r0 + 64 * r1), None)
    tmp1 = 1.0
    tmp2 = tmp0 - tmp1
    tmp4 = -tmp3
    tmp5 = libdevice.log1p(tmp4)
    tmp6 = -100.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp2 * tmp7
    tmp9 = tl_math.log(tmp3)
    tmp10 = triton_helpers.maximum(tmp9, tmp6)
    tmp11 = tmp0 * tmp10
    tmp12 = tmp8 - tmp11
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
    tmp15 = tl.sum(tmp13, 1)[:, None]
    tmp17 = tmp16 - tmp1
    tmp19 = -tmp18
    tmp20 = libdevice.log1p(tmp19)
    tmp21 = triton_helpers.maximum(tmp20, tmp6)
    tmp22 = tmp17 * tmp21
    tmp23 = tl_math.log(tmp18)
    tmp24 = triton_helpers.maximum(tmp23, tmp6)
    tmp25 = tmp16 * tmp24
    tmp26 = tmp22 - tmp25
    tmp27 = tl.broadcast_to(tmp26, [XBLOCK, RBLOCK])
    tmp29 = tl.sum(tmp27, 1)[:, None]
    tmp31 = tmp30 - tmp1
    tmp33 = -tmp32
    tmp34 = libdevice.log1p(tmp33)
    tmp35 = triton_helpers.maximum(tmp34, tmp6)
    tmp36 = tmp31 * tmp35
    tmp37 = tl_math.log(tmp32)
    tmp38 = triton_helpers.maximum(tmp37, tmp6)
    tmp39 = tmp30 * tmp38
    tmp40 = tmp36 - tmp39
    tmp41 = tl.broadcast_to(tmp40, [XBLOCK, RBLOCK])
    tmp43 = tl.sum(tmp41, 1)[:, None]
    tmp45 = tmp44 - tmp1
    tmp47 = -tmp46
    tmp48 = libdevice.log1p(tmp47)
    tmp49 = triton_helpers.maximum(tmp48, tmp6)
    tmp50 = tmp45 * tmp49
    tmp51 = tl_math.log(tmp46)
    tmp52 = triton_helpers.maximum(tmp51, tmp6)
    tmp53 = tmp44 * tmp52
    tmp54 = tmp50 - tmp53
    tmp55 = tl.broadcast_to(tmp54, [XBLOCK, RBLOCK])
    tmp57 = tl.sum(tmp55, 1)[:, None]
    tmp58 = 64.0
    tmp59 = tmp15 / tmp58
    tmp60 = 0.0
    tmp61 = tmp59 + tmp60
    tmp62 = tmp29 / tmp58
    tmp63 = tmp61 + tmp62
    tmp64 = tmp43 / tmp58
    tmp65 = tmp63 + tmp64
    tmp66 = tmp57 / tmp58
    tmp67 = tmp65 + tmp66
    tmp68 = 0.25
    tmp69 = tmp67 * tmp68
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp69, 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)
        buf4 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_add_binary_cross_entropy_div_0[grid(1)](buf4,
            arg1_1, arg0_1, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf4,


class GenerativeLossNew(nn.Module):

    def __init__(self):
        super(GenerativeLossNew, self).__init__()
        self.criterion = nn.BCELoss(reduction='mean')

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