# 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/hj/chjcbk2hdljkxzvt3ki2g7h34oz6yzea55wveanzmbe64apv4thx.py
# Topologically Sorted Source Nodes: [neg, probs_neg, pow_1, mul, mul_1, log_sigmoid, mul_2, probs_pos, pow_2, mul_3, sub, mul_4, log_sigmoid_1, neg_1, mul_5, loss_tmp], Original ATen: [aten.neg, aten.sigmoid, aten.pow, aten.mul, aten.log_sigmoid_forward, aten.rsub, aten.sub]
# Source node to ATen node mapping:
#   log_sigmoid => abs_1, exp, full_default, log1p, minimum, neg_1, sub
#   log_sigmoid_1 => abs_2, exp_1, full_default_1, log1p_1, minimum_1, neg_3, sub_2
#   loss_tmp => sub_3
#   mul => mul
#   mul_1 => mul_1
#   mul_2 => mul_2
#   mul_3 => mul_3
#   mul_4 => mul_4
#   mul_5 => mul_5
#   neg => neg
#   neg_1 => neg_2
#   pow_1 => pow_1
#   pow_2 => pow_2
#   probs_neg => sigmoid_1
#   probs_pos => sigmoid
#   sub => sub_1
# Graph fragment:
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%arg0_1,), kwargs = {})
#   %sigmoid_1 : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%neg,), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sigmoid_1, 2.0), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_1, -4), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %arg1_1), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %minimum : [num_users=1] = call_function[target=torch.ops.aten.minimum.default](args = (%full_default, %arg0_1), kwargs = {})
#   %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%arg0_1,), kwargs = {})
#   %neg_1 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%abs_1,), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%neg_1,), kwargs = {})
#   %log1p : [num_users=1] = call_function[target=torch.ops.aten.log1p.default](args = (%exp,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%minimum, %log1p), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_1, %sub), kwargs = {})
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%arg0_1,), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sigmoid, 2.0), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_2, -3), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.0, %arg1_1), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_3, %sub_1), kwargs = {})
#   %full_default_1 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %neg_2 : [num_users=2] = call_function[target=torch.ops.aten.neg.default](args = (%arg0_1,), kwargs = {})
#   %minimum_1 : [num_users=1] = call_function[target=torch.ops.aten.minimum.default](args = (%full_default_1, %neg_2), kwargs = {})
#   %abs_2 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%neg_2,), kwargs = {})
#   %neg_3 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%abs_2,), kwargs = {})
#   %exp_1 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%neg_3,), kwargs = {})
#   %log1p_1 : [num_users=1] = call_function[target=torch.ops.aten.log1p.default](args = (%exp_1,), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%minimum_1, %log1p_1), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_4, %sub_2), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_2, %mul_5), kwargs = {})
triton_poi_fused_log_sigmoid_forward_mul_neg_pow_rsub_sigmoid_sub_0 = async_compile.triton('triton_poi_fused_log_sigmoid_forward_mul_neg_pow_rsub_sigmoid_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.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_log_sigmoid_forward_mul_neg_pow_rsub_sigmoid_sub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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_log_sigmoid_forward_mul_neg_pow_rsub_sigmoid_sub_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp6 = tl.load(in_ptr1 + (x0), xmask)
    tmp1 = -tmp0
    tmp2 = tl.sigmoid(tmp1)
    tmp3 = tmp2 * tmp2
    tmp4 = -4.0
    tmp5 = tmp3 * tmp4
    tmp7 = tmp5 * tmp6
    tmp8 = 0.0
    tmp9 = triton_helpers.minimum(tmp8, tmp0)
    tmp10 = tl_math.abs(tmp0)
    tmp11 = -tmp10
    tmp12 = tl_math.exp(tmp11)
    tmp13 = libdevice.log1p(tmp12)
    tmp14 = tmp9 - tmp13
    tmp15 = tmp7 * tmp14
    tmp16 = tl.sigmoid(tmp0)
    tmp17 = tmp16 * tmp16
    tmp18 = -3.0
    tmp19 = tmp17 * tmp18
    tmp20 = 1.0
    tmp21 = tmp20 - tmp6
    tmp22 = tmp19 * tmp21
    tmp23 = triton_helpers.minimum(tmp8, tmp1)
    tmp24 = tl_math.abs(tmp1)
    tmp25 = -tmp24
    tmp26 = tl_math.exp(tmp25)
    tmp27 = libdevice.log1p(tmp26)
    tmp28 = tmp23 - tmp27
    tmp29 = tmp22 * tmp28
    tmp30 = tmp15 - tmp29
    tl.store(out_ptr0 + (x0), tmp30, xmask)
''', 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, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [neg, probs_neg, pow_1, mul, mul_1, log_sigmoid, mul_2, probs_pos, pow_2, mul_3, sub, mul_4, log_sigmoid_1, neg_1, mul_5, loss_tmp], Original ATen: [aten.neg, aten.sigmoid, aten.pow, aten.mul, aten.log_sigmoid_forward, aten.rsub, aten.sub]
        stream0 = get_raw_stream(0)
        triton_poi_fused_log_sigmoid_forward_mul_neg_pow_rsub_sigmoid_sub_0.run(arg0_1, arg1_1, buf0, 256, grid=grid(256), stream=stream0)
        del arg0_1
        del arg1_1
    return (buf0, )


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 warnings
from typing import Optional
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data


def binary_focal_loss_with_logits(input: 'torch.Tensor', target:
    'torch.Tensor', alpha: 'float'=0.25, gamma: 'float'=2.0, reduction:
    'str'='none', eps: 'Optional[float]'=None) ->torch.Tensor:
    """Function that computes Binary Focal loss.
    .. math::
        \\text{FL}(p_t) = -\\alpha_t (1 - p_t)^{\\gamma} \\, \\text{log}(p_t)
    where:
       - :math:`p_t` is the model's estimated probability for each class.
    Args:
        input: input data tensor of arbitrary shape.
        target: the target tensor with shape matching input.
        alpha: Weighting factor for the rare class :math:`\\alpha \\in [0, 1]`.
        gamma: Focusing parameter :math:`\\gamma >= 0`.
        reduction: Specifies the reduction to apply to the
          output: ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction
          will be applied, ``'mean'``: the sum of the output will be divided by
          the number of elements in the output, ``'sum'``: the output will be
          summed.
        eps: Deprecated: scalar for numerically stability when dividing. This is no longer used.
    Returns:
        the computed loss.
    Examples:
        >>> kwargs = {"alpha": 0.25, "gamma": 2.0, "reduction": 'mean'}
        >>> logits = torch.tensor([[[6.325]],[[5.26]],[[87.49]]])
        >>> labels = torch.tensor([[[1.]],[[1.]],[[0.]]])
        >>> binary_focal_loss_with_logits(logits, labels, **kwargs)
        tensor(21.8725)
    """
    if eps is not None and not torch.jit.is_scripting():
        warnings.warn(
            '`binary_focal_loss_with_logits` has been reworked for improved numerical stability and the `eps` argument is no longer necessary'
            , DeprecationWarning, stacklevel=2)
    if not isinstance(input, torch.Tensor):
        raise TypeError(f'Input type is not a torch.Tensor. Got {type(input)}')
    if not len(input.shape) >= 2:
        raise ValueError(
            f'Invalid input shape, we expect BxCx*. Got: {input.shape}')
    if input.size(0) != target.size(0):
        raise ValueError(
            f'Expected input batch_size ({input.size(0)}) to match target batch_size ({target.size(0)}).'
            )
    probs_pos = torch.sigmoid(input)
    probs_neg = torch.sigmoid(-input)
    loss_tmp = -alpha * torch.pow(probs_neg, gamma) * target * F.logsigmoid(
        input) - (1 - alpha) * torch.pow(probs_pos, gamma) * (1.0 - target
        ) * F.logsigmoid(-input)
    if reduction == 'none':
        loss = loss_tmp
    elif reduction == 'mean':
        loss = torch.mean(loss_tmp)
    elif reduction == 'sum':
        loss = torch.sum(loss_tmp)
    else:
        raise NotImplementedError(f'Invalid reduction mode: {reduction}')
    return loss


class BinaryFocalLossWithLogits(nn.Module):
    """Criterion that computes Focal loss.
    According to :cite:`lin2018focal`, the Focal loss is computed as follows:
    .. math::
        \\text{FL}(p_t) = -\\alpha_t (1 - p_t)^{\\gamma} \\, \\text{log}(p_t)
    where:
       - :math:`p_t` is the model's estimated probability for each class.
    Args:
        alpha): Weighting factor for the rare class :math:`\\alpha \\in [0, 1]`.
        gamma: Focusing parameter :math:`\\gamma >= 0`.
        reduction: Specifies the reduction to apply to the
          output: ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction
          will be applied, ``'mean'``: the sum of the output will be divided by
          the number of elements in the output, ``'sum'``: the output will be
          summed.
    Shape:
        - Input: :math:`(N, *)`.
        - Target: :math:`(N, *)`.
    Examples:
        >>> kwargs = {"alpha": 0.25, "gamma": 2.0, "reduction": 'mean'}
        >>> loss = BinaryFocalLossWithLogits(**kwargs)
        >>> input = torch.randn(1, 3, 5, requires_grad=True)
        >>> target = torch.empty(1, 3, 5, dtype=torch.long).random_(2)
        >>> output = loss(input, target)
        >>> output.backward()
    """

    def __init__(self, alpha: 'float', gamma: 'float'=2.0, reduction: 'str'
        ='none') ->None:
        super().__init__()
        self.alpha: 'float' = alpha
        self.gamma: 'float' = gamma
        self.reduction: 'str' = reduction

    def forward(self, input: 'torch.Tensor', target: 'torch.Tensor'
        ) ->torch.Tensor:
        return binary_focal_loss_with_logits(input, target, self.alpha,
            self.gamma, self.reduction)


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {'alpha': 4}]

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 warnings
from typing import Optional
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
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_log_sigmoid_forward_mul_neg_pow_rsub_sigmoid_sub_0(in_ptr0
    , in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp6 = tl.load(in_ptr1 + x0, xmask)
    tmp1 = -tmp0
    tmp2 = tl.sigmoid(tmp1)
    tmp3 = tmp2 * tmp2
    tmp4 = -4.0
    tmp5 = tmp3 * tmp4
    tmp7 = tmp5 * tmp6
    tmp8 = 0.0
    tmp9 = triton_helpers.minimum(tmp8, tmp0)
    tmp10 = tl_math.abs(tmp0)
    tmp11 = -tmp10
    tmp12 = tl_math.exp(tmp11)
    tmp13 = libdevice.log1p(tmp12)
    tmp14 = tmp9 - tmp13
    tmp15 = tmp7 * tmp14
    tmp16 = tl.sigmoid(tmp0)
    tmp17 = tmp16 * tmp16
    tmp18 = -3.0
    tmp19 = tmp17 * tmp18
    tmp20 = 1.0
    tmp21 = tmp20 - tmp6
    tmp22 = tmp19 * tmp21
    tmp23 = triton_helpers.minimum(tmp8, tmp1)
    tmp24 = tl_math.abs(tmp1)
    tmp25 = -tmp24
    tmp26 = tl_math.exp(tmp25)
    tmp27 = libdevice.log1p(tmp26)
    tmp28 = tmp23 - tmp27
    tmp29 = tmp22 * tmp28
    tmp30 = tmp15 - tmp29
    tl.store(out_ptr0 + x0, tmp30, xmask)


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, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_log_sigmoid_forward_mul_neg_pow_rsub_sigmoid_sub_0[
            grid(256)](arg0_1, arg1_1, buf0, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del arg0_1
        del arg1_1
    return buf0,


def binary_focal_loss_with_logits(input: 'torch.Tensor', target:
    'torch.Tensor', alpha: 'float'=0.25, gamma: 'float'=2.0, reduction:
    'str'='none', eps: 'Optional[float]'=None) ->torch.Tensor:
    """Function that computes Binary Focal loss.
    .. math::
        \\text{FL}(p_t) = -\\alpha_t (1 - p_t)^{\\gamma} \\, \\text{log}(p_t)
    where:
       - :math:`p_t` is the model's estimated probability for each class.
    Args:
        input: input data tensor of arbitrary shape.
        target: the target tensor with shape matching input.
        alpha: Weighting factor for the rare class :math:`\\alpha \\in [0, 1]`.
        gamma: Focusing parameter :math:`\\gamma >= 0`.
        reduction: Specifies the reduction to apply to the
          output: ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction
          will be applied, ``'mean'``: the sum of the output will be divided by
          the number of elements in the output, ``'sum'``: the output will be
          summed.
        eps: Deprecated: scalar for numerically stability when dividing. This is no longer used.
    Returns:
        the computed loss.
    Examples:
        >>> kwargs = {"alpha": 0.25, "gamma": 2.0, "reduction": 'mean'}
        >>> logits = torch.tensor([[[6.325]],[[5.26]],[[87.49]]])
        >>> labels = torch.tensor([[[1.]],[[1.]],[[0.]]])
        >>> binary_focal_loss_with_logits(logits, labels, **kwargs)
        tensor(21.8725)
    """
    if eps is not None and not torch.jit.is_scripting():
        warnings.warn(
            '`binary_focal_loss_with_logits` has been reworked for improved numerical stability and the `eps` argument is no longer necessary'
            , DeprecationWarning, stacklevel=2)
    if not isinstance(input, torch.Tensor):
        raise TypeError(f'Input type is not a torch.Tensor. Got {type(input)}')
    if not len(input.shape) >= 2:
        raise ValueError(
            f'Invalid input shape, we expect BxCx*. Got: {input.shape}')
    if input.size(0) != target.size(0):
        raise ValueError(
            f'Expected input batch_size ({input.size(0)}) to match target batch_size ({target.size(0)}).'
            )
    probs_pos = torch.sigmoid(input)
    probs_neg = torch.sigmoid(-input)
    loss_tmp = -alpha * torch.pow(probs_neg, gamma) * target * F.logsigmoid(
        input) - (1 - alpha) * torch.pow(probs_pos, gamma) * (1.0 - target
        ) * F.logsigmoid(-input)
    if reduction == 'none':
        loss = loss_tmp
    elif reduction == 'mean':
        loss = torch.mean(loss_tmp)
    elif reduction == 'sum':
        loss = torch.sum(loss_tmp)
    else:
        raise NotImplementedError(f'Invalid reduction mode: {reduction}')
    return loss


class BinaryFocalLossWithLogitsNew(nn.Module):
    """Criterion that computes Focal loss.
    According to :cite:`lin2018focal`, the Focal loss is computed as follows:
    .. math::
        \\text{FL}(p_t) = -\\alpha_t (1 - p_t)^{\\gamma} \\, \\text{log}(p_t)
    where:
       - :math:`p_t` is the model's estimated probability for each class.
    Args:
        alpha): Weighting factor for the rare class :math:`\\alpha \\in [0, 1]`.
        gamma: Focusing parameter :math:`\\gamma >= 0`.
        reduction: Specifies the reduction to apply to the
          output: ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction
          will be applied, ``'mean'``: the sum of the output will be divided by
          the number of elements in the output, ``'sum'``: the output will be
          summed.
    Shape:
        - Input: :math:`(N, *)`.
        - Target: :math:`(N, *)`.
    Examples:
        >>> kwargs = {"alpha": 0.25, "gamma": 2.0, "reduction": 'mean'}
        >>> loss = BinaryFocalLossWithLogits(**kwargs)
        >>> input = torch.randn(1, 3, 5, requires_grad=True)
        >>> target = torch.empty(1, 3, 5, dtype=torch.long).random_(2)
        >>> output = loss(input, target)
        >>> output.backward()
    """

    def __init__(self, alpha: 'float', gamma: 'float'=2.0, reduction: 'str'
        ='none') ->None:
        super().__init__()
        self.alpha: 'float' = alpha
        self.gamma: 'float' = gamma
        self.reduction: 'str' = reduction

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