# AOT ID: ['0_forward']
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/xs/cxshdstuiqvf6vwig3tx5gvibyp7xmydycbesv25izblg5r4pe7f.py
# Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   softmax => amax, exp, sub, sum_1
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%primals_2, [0], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%primals_2, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [0], True), kwargs = {})
triton_per_fused__softmax_0 = async_compile.triton('triton_per_fused__softmax_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, 4],
    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), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__softmax_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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__softmax_0(in_ptr0, out_ptr0, out_ptr1, 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 + (r0), None)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = triton_helpers.max2(tmp1, 1)[:, None]
    tmp4 = tmp0 - tmp3
    tmp5 = tl_math.exp(tmp4)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp8 = tl.sum(tmp6, 1)[:, None]
    tl.store(out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp3, None)
    tl.store(out_ptr1 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp8, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/qa/cqa6fq6w6j5giqc2jtriipt6q4bhbvpvxpil2uumt4typbzf76jb.py
# Topologically Sorted Source Nodes: [softmax, z_1, z_2], Original ATen: [aten._softmax, aten.mul, aten.sum]
# Source node to ATen node mapping:
#   softmax => div, exp, sub
#   z_1 => mul
#   z_2 => sum_2
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%primals_2, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_1, %div), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [1]), kwargs = {})
triton_poi_fused__softmax_mul_sum_1 = async_compile.triton('triton_poi_fused__softmax_mul_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.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_mul_sum_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 7, '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__softmax_mul_sum_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = (xindex // 16)
    x3 = xindex % 16
    x0 = xindex % 4
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x3 + (64*x2)), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + (0))
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK])
    tmp6 = tl.load(in_ptr3 + (0))
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
    tmp10 = tl.load(in_ptr0 + (16 + x3 + (64*x2)), xmask)
    tmp13 = tl.load(in_ptr0 + (32 + x3 + (64*x2)), xmask)
    tmp16 = tl.load(in_ptr0 + (48 + x3 + (64*x2)), xmask)
    tmp4 = tmp1 - tmp3
    tmp5 = tl_math.exp(tmp4)
    tmp8 = tmp5 / tmp7
    tmp9 = tmp0 * tmp8
    tmp11 = tmp10 * tmp8
    tmp12 = tmp9 + tmp11
    tmp14 = tmp13 * tmp8
    tmp15 = tmp12 + tmp14
    tmp17 = tmp16 * tmp8
    tmp18 = tmp15 + tmp17
    tl.store(out_ptr0 + (x4), tmp18, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1, ), (1, ), torch.float32)
        buf1 = empty_strided_cuda((1, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax]
        stream0 = get_raw_stream(0)
        triton_per_fused__softmax_0.run(primals_2, buf0, buf1, 1, 4, grid=grid(1), stream=stream0)
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [softmax, z_1, z_2], Original ATen: [aten._softmax, aten.mul, aten.sum]
        triton_poi_fused__softmax_mul_sum_1.run(primals_1, primals_2, buf0, buf1, buf2, 64, grid=grid(64), stream=stream0)
        del buf0
        del buf1
    return (buf2, primals_1, primals_2, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2])
    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 numpy as np
from torch import nn
from torch.utils.data import DataLoader
import torch.nn.functional as F
from torchvision import transforms
from sklearn.preprocessing import StandardScaler
from sklearn import metrics
from torch.utils.data import Dataset


def compute_auc(labels, scores, pos_label=1):
    fpr, tpr, _thresholds = metrics.roc_curve(labels, scores, pos_label=
        pos_label)
    return metrics.auc(fpr, tpr)


class Subset(Dataset):

    def __init__(self, data, labels, normalize=False):
        self.ims = data
        self.labels = labels
        self.normalize = normalize
        if normalize:
            self.T = transforms.Normalize(0.5, 0.5)
        else:
            self.T = lambda x: x

    def __getitem__(self, idx):
        ret = {'ims': self.T(self.ims[idx]), 'labels': self.labels[idx]}
        return ret

    def __len__(self):
        return self.labels.shape[0]


class AlphaClassifier(nn.Module):

    def __init__(self):
        super(AlphaClassifier, self).__init__()
        self.alpha_params = nn.Parameter(torch.Tensor(np.ones(4)))
        self.scaler = StandardScaler()

    def get_alpha(self):
        return F.softmax(self.alpha_params, dim=0)

    def forward(self, x):
        z = torch.Tensor(x)
        z = z * self.get_alpha()
        z = z.sum(1)
        return z

    def predict_prob(self, x):
        return torch.sigmoid(self(x))

    def predict(self, x):
        return torch.round(self.predict_prob(x))

    def binary_acc(self, x, y):
        y_true = np.array(y)
        y_pred = self.predict(x).detach().numpy()
        nhits = (y_pred == y_true).sum()
        return nhits / y_true.shape[0]

    def auc(self, x, y):
        scores = self.predict_prob(x)
        return compute_auc(y, scores.detach().numpy())

    def scaler_fit(self, x):
        self.scaler.fit(x)

    def scaler_transform(self, x):
        return self.scaler.transform(x)

    def save_weights(self, f):
        np.save(f, self.alpha_params.detach().numpy())

    def fit(self, x, y, tst_x=None, tst_y=None, nepochs=200, batch_size=256,
        lr=0.001, workers=1, balanced=True, verb=True, scale=False,
        early_stopping=False, patience=10):
        if scale:
            self.scaler_fit(x)
            x = self.scaler_transform(x)
            tst_x = tst_x if tst_x is None else self.scaler_transform(tst_x)
        if balanced:
            n1 = int(sum(y))
            n0 = len(y) - n1
            if n0 < n1:
                p = int(np.floor(n1 / n0))
                X = np.concatenate((x[y == 0].repeat(p, 0), x[y == 1]), 0)
                Y = np.concatenate((y[y == 0].repeat(p, 0), y[y == 1]), 0)
            else:
                p = int(np.floor(n0 / n1))
                X = np.concatenate((x[y == 1].repeat(p, 0), x[y == 0]), 0)
                Y = np.concatenate((y[y == 1].repeat(p, 0), y[y == 0]), 0)
        else:
            X = x
            Y = y
        loader = DataLoader(Subset(torch.tensor(X).float(), torch.Tensor(Y)
            ), batch_size=batch_size, shuffle=True, num_workers=workers)
        criterion = nn.BCEWithLogitsLoss()
        opt = torch.optim.Adam(self.parameters(), lr=lr)
        best_auc = self.auc(x, y)
        pat = 0
        for epoch in range(nepochs):
            if verb:
                criterion(self(torch.Tensor(x)), torch.Tensor(y)).detach(
                    ).numpy().round(3)
                self.auc(x, y).round(3)
                self.binary_acc(x, y).round(3)
                np.NAN if tst_x is None else self.auc(tst_x, tst_y).round(3)
                None
            if early_stopping:
                cur_auc = self.auc(x, y)
                if cur_auc < best_auc:
                    if pat < patience:
                        pat += 1
                    else:
                        if verb:
                            None
                        return
                else:
                    best_auc = cur_auc
                    pat = 0
            for batch in loader:
                _x, _y = batch['ims'], batch['labels']
                opt.zero_grad()
                pred = self(_x)
                loss = criterion(pred, _y)
                loss.backward()
                opt.step()


def get_inputs():
    return [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 numpy as np
from torch import nn
from torch.utils.data import DataLoader
import torch.nn.functional as F
from torchvision import transforms
from sklearn.preprocessing import StandardScaler
from sklearn import metrics
from torch.utils.data import Dataset
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__softmax_0(in_ptr0, out_ptr0, out_ptr1, 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 + r0, None)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = triton_helpers.max2(tmp1, 1)[:, None]
    tmp4 = tmp0 - tmp3
    tmp5 = tl_math.exp(tmp4)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp8 = tl.sum(tmp6, 1)[:, None]
    tl.store(out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp3, None)
    tl.store(out_ptr1 + tl.full([XBLOCK, 1], 0, tl.int32), tmp8, None)


@triton.jit
def triton_poi_fused__softmax_mul_sum_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex // 16
    x3 = xindex % 16
    x0 = xindex % 4
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x3 + 64 * x2), xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + 0)
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK])
    tmp6 = tl.load(in_ptr3 + 0)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
    tmp10 = tl.load(in_ptr0 + (16 + x3 + 64 * x2), xmask)
    tmp13 = tl.load(in_ptr0 + (32 + x3 + 64 * x2), xmask)
    tmp16 = tl.load(in_ptr0 + (48 + x3 + 64 * x2), xmask)
    tmp4 = tmp1 - tmp3
    tmp5 = tl_math.exp(tmp4)
    tmp8 = tmp5 / tmp7
    tmp9 = tmp0 * tmp8
    tmp11 = tmp10 * tmp8
    tmp12 = tmp9 + tmp11
    tmp14 = tmp13 * tmp8
    tmp15 = tmp12 + tmp14
    tmp17 = tmp16 * tmp8
    tmp18 = tmp15 + tmp17
    tl.store(out_ptr0 + x4, tmp18, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1,), (1,), torch.float32)
        buf1 = empty_strided_cuda((1,), (1,), torch.float32)
        get_raw_stream(0)
        triton_per_fused__softmax_0[grid(1)](primals_2, buf0, buf1, 1, 4,
            XBLOCK=1, num_warps=2, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_mul_sum_1[grid(64)](primals_1, primals_2,
            buf0, buf1, buf2, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf0
        del buf1
    return buf2, primals_1, primals_2


def compute_auc(labels, scores, pos_label=1):
    fpr, tpr, _thresholds = metrics.roc_curve(labels, scores, pos_label=
        pos_label)
    return metrics.auc(fpr, tpr)


class Subset(Dataset):

    def __init__(self, data, labels, normalize=False):
        self.ims = data
        self.labels = labels
        self.normalize = normalize
        if normalize:
            self.T = transforms.Normalize(0.5, 0.5)
        else:
            self.T = lambda x: x

    def __getitem__(self, idx):
        ret = {'ims': self.T(self.ims[idx]), 'labels': self.labels[idx]}
        return ret

    def __len__(self):
        return self.labels.shape[0]


class AlphaClassifierNew(nn.Module):

    def __init__(self):
        super(AlphaClassifierNew, self).__init__()
        self.alpha_params = nn.Parameter(torch.Tensor(np.ones(4)))
        self.scaler = StandardScaler()

    def get_alpha(self):
        return F.softmax(self.alpha_params, dim=0)

    def predict_prob(self, x):
        return torch.sigmoid(self(x))

    def predict(self, x):
        return torch.round(self.predict_prob(x))

    def binary_acc(self, x, y):
        y_true = np.array(y)
        y_pred = self.predict(x).detach().numpy()
        nhits = (y_pred == y_true).sum()
        return nhits / y_true.shape[0]

    def auc(self, x, y):
        scores = self.predict_prob(x)
        return compute_auc(y, scores.detach().numpy())

    def scaler_fit(self, x):
        self.scaler.fit(x)

    def scaler_transform(self, x):
        return self.scaler.transform(x)

    def save_weights(self, f):
        np.save(f, self.alpha_params.detach().numpy())

    def fit(self, x, y, tst_x=None, tst_y=None, nepochs=200, batch_size=256,
        lr=0.001, workers=1, balanced=True, verb=True, scale=False,
        early_stopping=False, patience=10):
        if scale:
            self.scaler_fit(x)
            x = self.scaler_transform(x)
            tst_x = tst_x if tst_x is None else self.scaler_transform(tst_x)
        if balanced:
            n1 = int(sum(y))
            n0 = len(y) - n1
            if n0 < n1:
                p = int(np.floor(n1 / n0))
                X = np.concatenate((x[y == 0].repeat(p, 0), x[y == 1]), 0)
                Y = np.concatenate((y[y == 0].repeat(p, 0), y[y == 1]), 0)
            else:
                p = int(np.floor(n0 / n1))
                X = np.concatenate((x[y == 1].repeat(p, 0), x[y == 0]), 0)
                Y = np.concatenate((y[y == 1].repeat(p, 0), y[y == 0]), 0)
        else:
            X = x
            Y = y
        loader = DataLoader(Subset(torch.tensor(X).float(), torch.Tensor(Y)
            ), batch_size=batch_size, shuffle=True, num_workers=workers)
        criterion = nn.BCEWithLogitsLoss()
        opt = torch.optim.Adam(self.parameters(), lr=lr)
        best_auc = self.auc(x, y)
        pat = 0
        for epoch in range(nepochs):
            if verb:
                criterion(self(torch.Tensor(x)), torch.Tensor(y)).detach(
                    ).numpy().round(3)
                self.auc(x, y).round(3)
                self.binary_acc(x, y).round(3)
                np.NAN if tst_x is None else self.auc(tst_x, tst_y).round(3)
                None
            if early_stopping:
                cur_auc = self.auc(x, y)
                if cur_auc < best_auc:
                    if pat < patience:
                        pat += 1
                    else:
                        if verb:
                            None
                        return
                else:
                    best_auc = cur_auc
                    pat = 0
            for batch in loader:
                _x, _y = batch['ims'], batch['labels']
                opt.zero_grad()
                pred = self(_x)
                loss = criterion(pred, _y)
                loss.backward()
                opt.step()

    def forward(self, input_0):
        primals_2 = self.alpha_params
        primals_1 = input_0
        output = call([primals_1, primals_2])
        return output[0]
