# 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/og/cog3kd7uhemqvd6ipiyvguzqznhktmd2cwizp3t2x2osxavv56r2.py
# Topologically Sorted Source Nodes: [l1_loss, cosine_similarity], Original ATen: [aten.sub, aten.abs, aten.mean, aten.linalg_vector_norm, aten.clamp_min, aten.div, aten.mul]
# Source node to ATen node mapping:
#   cosine_similarity => clamp_min, clamp_min_1, div, div_1, mul, pow_1, pow_2, pow_3, pow_4, sum_1, sum_2
#   l1_loss => abs_1, mean_1, sub_1
# Graph fragment:
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg1_1, %arg0_1), kwargs = {})
#   %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%sub_1,), kwargs = {})
#   %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%abs_1,), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%arg1_1, 2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [1], True), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_1, 0.5), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%pow_2, 1e-20), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%arg1_1, %clamp_min), kwargs = {})
#   %pow_3 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%arg0_1, 2), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_3, [1], True), kwargs = {})
#   %pow_4 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_2, 0.5), kwargs = {})
#   %clamp_min_1 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%pow_4, 1e-20), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%arg0_1, %clamp_min_1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div_1, %div), kwargs = {})
triton_per_fused_abs_clamp_min_div_linalg_vector_norm_mean_mul_sub_0 = async_compile.triton('triton_per_fused_abs_clamp_min_div_linalg_vector_norm_mean_mul_sub_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, 256],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: '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': {4: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 5), equal_to_1=(4,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_abs_clamp_min_div_linalg_vector_norm_mean_mul_sub_0', 'mutated_arg_names': [], 'no_x_dim': True, 'num_load': 10, '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_abs_clamp_min_div_linalg_vector_norm_mean_mul_sub_0(in_ptr0, in_ptr1, out_ptr0, out_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
    r1 = rindex % 16
    r3 = (rindex // 64)
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp1 = tl.load(in_ptr1 + (r0), None)
    tmp7 = tl.load(in_ptr0 + (r1 + (64*r3)), None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (16 + r1 + (64*r3)), None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (32 + r1 + (64*r3)), None, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr0 + (48 + r1 + (64*r3)), None, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr1 + (r1 + (64*r3)), None, eviction_policy='evict_last')
    tmp24 = tl.load(in_ptr1 + (16 + r1 + (64*r3)), None, eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr1 + (32 + r1 + (64*r3)), None, eviction_policy='evict_last')
    tmp30 = tl.load(in_ptr1 + (48 + r1 + (64*r3)), None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp3 = tl_math.abs(tmp2)
    tmp4 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp6 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0))
    tmp8 = tmp7 * tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp13 = tmp12 * tmp12
    tmp14 = tmp11 + tmp13
    tmp16 = tmp15 * tmp15
    tmp17 = tmp14 + tmp16
    tmp18 = libdevice.sqrt(tmp17)
    tmp19 = 1e-20
    tmp20 = triton_helpers.maximum(tmp18, tmp19)
    tmp21 = tmp0 / tmp20
    tmp23 = tmp22 * tmp22
    tmp25 = tmp24 * tmp24
    tmp26 = tmp23 + tmp25
    tmp28 = tmp27 * tmp27
    tmp29 = tmp26 + tmp28
    tmp31 = tmp30 * tmp30
    tmp32 = tmp29 + tmp31
    tmp33 = libdevice.sqrt(tmp32)
    tmp34 = triton_helpers.maximum(tmp33, tmp19)
    tmp35 = tmp1 / tmp34
    tmp36 = tmp21 * tmp35
    tl.store(out_ptr1 + (tl.broadcast_to(r0, [RBLOCK])), tmp36, None)
    tl.store(out_ptr0 + (tl.full([1], 0, tl.int32)), tmp6, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/fx/cfxynxpfycn6aambt5gmizly5ce4gdie5gjtdbc7xxbhmvf3qkk6.py
# Topologically Sorted Source Nodes: [l1_loss, cosine_similarity, sub, cosine_term, mul, add], Original ATen: [aten.sub, aten.abs, aten.mean, aten.sum, aten.rsub, aten.mul, aten.add]
# Source node to ATen node mapping:
#   add => add
#   cosine_similarity => sum_3
#   cosine_term => mean
#   l1_loss => abs_1, mean_1, sub_1
#   mul => mul_1
#   sub => sub
# Graph fragment:
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg1_1, %arg0_1), kwargs = {})
#   %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%sub_1,), kwargs = {})
#   %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%abs_1,), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [1]), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %sum_3), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sub,), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mean, 5), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mean_1, %mul_1), kwargs = {})
triton_per_fused_abs_add_mean_mul_rsub_sub_sum_1 = async_compile.triton('triton_per_fused_abs_add_mean_mul_rsub_sub_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, 64],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: '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': {2: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 3), equal_to_1=(2,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_abs_add_mean_mul_rsub_sub_sum_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 5, '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_abs_add_mean_mul_rsub_sub_sum_1(in_out_ptr0, in_ptr0, 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)
    tmp1 = tl.load(in_ptr0 + (16 + r0 + (64*r1)), None)
    tmp3 = tl.load(in_ptr0 + (32 + r0 + (64*r1)), None)
    tmp5 = tl.load(in_ptr0 + (48 + r0 + (64*r1)), None)
    tmp12 = tl.load(in_out_ptr0 + (0))
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, 1])
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 1.0
    tmp8 = tmp7 - tmp6
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK])
    tmp11 = tl.sum(tmp9, 1)[:, None]
    tmp14 = 256.0
    tmp15 = tmp13 / tmp14
    tmp16 = 64.0
    tmp17 = tmp11 / tmp16
    tmp18 = 5.0
    tmp19 = tmp17 * tmp18
    tmp20 = tmp15 + tmp19
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp20, 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)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [l1_loss, cosine_similarity], Original ATen: [aten.sub, aten.abs, aten.mean, aten.linalg_vector_norm, aten.clamp_min, aten.div, aten.mul]
        stream0 = get_raw_stream(0)
        triton_per_fused_abs_clamp_min_div_linalg_vector_norm_mean_mul_sub_0.run(arg1_1, arg0_1, buf0, buf1, 1, 256, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
        buf3 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [l1_loss, cosine_similarity, sub, cosine_term, mul, add], Original ATen: [aten.sub, aten.abs, aten.mean, aten.sum, aten.rsub, aten.mul, aten.add]
        triton_per_fused_abs_add_mean_mul_rsub_sub_sum_1.run(buf3, buf1, 1, 64, grid=grid(1), stream=stream0)
        del buf1
    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)
    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 torch import nn


class ExpandNetLoss(nn.Module):

    def __init__(self, loss_lambda=5):
        super(ExpandNetLoss, self).__init__()
        self.similarity = torch.nn.CosineSimilarity(dim=1, eps=1e-20)
        self.l1_loss = nn.L1Loss()
        self.loss_lambda = loss_lambda

    def forward(self, x, y):
        cosine_term = (1 - self.similarity(x, y)).mean()
        return self.l1_loss(x, y) + self.loss_lambda * cosine_term


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
from torch import 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_clamp_min_div_linalg_vector_norm_mean_mul_sub_0(
    in_ptr0, in_ptr1, out_ptr0, out_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
    r1 = rindex % 16
    r3 = rindex // 64
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tl.load(in_ptr1 + r0, None)
    tmp7 = tl.load(in_ptr0 + (r1 + 64 * r3), None, eviction_policy='evict_last'
        )
    tmp9 = tl.load(in_ptr0 + (16 + r1 + 64 * r3), None, eviction_policy=
        'evict_last')
    tmp12 = tl.load(in_ptr0 + (32 + r1 + 64 * r3), None, eviction_policy=
        'evict_last')
    tmp15 = tl.load(in_ptr0 + (48 + r1 + 64 * r3), None, eviction_policy=
        'evict_last')
    tmp22 = tl.load(in_ptr1 + (r1 + 64 * r3), None, eviction_policy=
        'evict_last')
    tmp24 = tl.load(in_ptr1 + (16 + r1 + 64 * r3), None, eviction_policy=
        'evict_last')
    tmp27 = tl.load(in_ptr1 + (32 + r1 + 64 * r3), None, eviction_policy=
        'evict_last')
    tmp30 = tl.load(in_ptr1 + (48 + r1 + 64 * r3), None, eviction_policy=
        'evict_last')
    tmp2 = tmp0 - tmp1
    tmp3 = tl_math.abs(tmp2)
    tmp4 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp6 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0))
    tmp8 = tmp7 * tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp13 = tmp12 * tmp12
    tmp14 = tmp11 + tmp13
    tmp16 = tmp15 * tmp15
    tmp17 = tmp14 + tmp16
    tmp18 = libdevice.sqrt(tmp17)
    tmp19 = 1e-20
    tmp20 = triton_helpers.maximum(tmp18, tmp19)
    tmp21 = tmp0 / tmp20
    tmp23 = tmp22 * tmp22
    tmp25 = tmp24 * tmp24
    tmp26 = tmp23 + tmp25
    tmp28 = tmp27 * tmp27
    tmp29 = tmp26 + tmp28
    tmp31 = tmp30 * tmp30
    tmp32 = tmp29 + tmp31
    tmp33 = libdevice.sqrt(tmp32)
    tmp34 = triton_helpers.maximum(tmp33, tmp19)
    tmp35 = tmp1 / tmp34
    tmp36 = tmp21 * tmp35
    tl.store(out_ptr1 + tl.broadcast_to(r0, [RBLOCK]), tmp36, None)
    tl.store(out_ptr0 + tl.full([1], 0, tl.int32), tmp6, None)


@triton.jit
def triton_per_fused_abs_add_mean_mul_rsub_sub_sum_1(in_out_ptr0, in_ptr0,
    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)
    tmp1 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
    tmp3 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
    tmp5 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
    tmp12 = tl.load(in_out_ptr0 + 0)
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, 1])
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 1.0
    tmp8 = tmp7 - tmp6
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK])
    tmp11 = tl.sum(tmp9, 1)[:, None]
    tmp14 = 256.0
    tmp15 = tmp13 / tmp14
    tmp16 = 64.0
    tmp17 = tmp11 / tmp16
    tmp18 = 5.0
    tmp19 = tmp17 * tmp18
    tmp20 = tmp15 + tmp19
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp20, 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)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused_abs_clamp_min_div_linalg_vector_norm_mean_mul_sub_0[
            grid(1)](arg1_1, arg0_1, buf0, buf1, 1, 256, num_warps=2,
            num_stages=1)
        del arg0_1
        del arg1_1
        buf3 = buf0
        del buf0
        triton_per_fused_abs_add_mean_mul_rsub_sub_sum_1[grid(1)](buf3,
            buf1, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del buf1
    return buf3,


class ExpandNetLossNew(nn.Module):

    def __init__(self, loss_lambda=5):
        super(ExpandNetLossNew, self).__init__()
        self.similarity = torch.nn.CosineSimilarity(dim=1, eps=1e-20)
        self.l1_loss = nn.L1Loss()
        self.loss_lambda = loss_lambda

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