# 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/2l/c2lqz5eg4uounnzhu2xhx6kieol5nd5d676uuijy3vfi2vfxmwhd.py
# Topologically Sorted Source Nodes: [eye, labels, pow_1, sum_1, v_norm, sub, relu, mul, sub_1, mul_1, sub_2, relu_1, mul_2, loss], Original ATen: [aten.eye, aten.index, aten.pow, aten.sum, aten.sqrt, aten.rsub, aten.relu, aten.mul, aten.sub, aten.add]
# Source node to ATen node mapping:
#   eye => eq, full_default, full_default_1, iota_1, where
#   labels => index
#   loss => add
#   mul => mul
#   mul_1 => mul_1
#   mul_2 => mul_2
#   pow_1 => pow_1
#   relu => relu
#   relu_1 => relu_1
#   sub => sub
#   sub_1 => sub_1
#   sub_2 => sub_2
#   sum_1 => sum_1
#   v_norm => sqrt
# Graph fragment:
#   %iota_1 : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (4,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
#   %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Tensor](args = (%unsqueeze, %iota_1), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([1], 1), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %full_default_1 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%eq, %full_default, %full_default_1), kwargs = {})
#   %index : [num_users=2] = call_function[target=torch.ops.aten.index.Tensor](args = (%where, [%arg1_1]), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%arg0_1, 2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [-1]), kwargs = {})
#   %sqrt : [num_users=2] = call_function[target=torch.ops.aten.sqrt.default](args = (%sum_1,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (0.9, %sqrt), kwargs = {})
#   %relu : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%sub,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%index, %relu), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.0, %index), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, 0.5), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sqrt, 0.1), kwargs = {})
#   %relu_1 : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%sub_2,), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_1, %relu_1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_2), kwargs = {})
triton_poi_fused_add_eye_index_mul_pow_relu_rsub_sqrt_sub_sum_0 = async_compile.triton('triton_poi_fused_add_eye_index_mul_pow_relu_rsub_sqrt_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 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_add_eye_index_mul_pow_relu_rsub_sqrt_sub_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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_add_eye_index_mul_pow_relu_rsub_sqrt_sub_sum_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4) % 4
    x0 = xindex % 4
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (4*x3), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr1 + (1 + (4*x3)), xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr1 + (2 + (4*x3)), xmask, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr1 + (3 + (4*x3)), xmask, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 4, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert(((0 <= tmp4) & (tmp4 < 4)) | ~(xmask), "index out of bounds: 0 <= tmp4 < 4")
    tmp6 = tmp4
    tmp7 = x0
    tmp8 = tmp6 == tmp7
    tmp9 = 1.0
    tmp10 = 0.0
    tmp11 = tl.where(tmp8, tmp9, tmp10)
    tmp13 = tmp12 * tmp12
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp21 = tmp20 * tmp20
    tmp22 = tmp19 + tmp21
    tmp23 = libdevice.sqrt(tmp22)
    tmp24 = 0.9
    tmp25 = tmp24 - tmp23
    tmp26 = tl.full([1], 0, tl.int32)
    tmp27 = triton_helpers.maximum(tmp26, tmp25)
    tmp28 = tmp11 * tmp27
    tmp29 = tmp9 - tmp11
    tmp30 = 0.5
    tmp31 = tmp29 * tmp30
    tmp32 = 0.1
    tmp33 = tmp23 - tmp32
    tmp34 = triton_helpers.maximum(tmp26, tmp33)
    tmp35 = tmp31 * tmp34
    tmp36 = tmp28 + tmp35
    tl.store(out_ptr0 + (x3), tmp36, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/nl/cnlounivewsan7neintygju4ung6fdl7sne7vd5zvvrfxzgid2yx.py
# Topologically Sorted Source Nodes: [sum_2, mean], Original ATen: [aten.sum, aten.mean]
# Source node to ATen node mapping:
#   mean => mean
#   sum_2 => sum_2
# Graph fragment:
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%add, [-1]), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sum_2,), 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, 16],
    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_mean_sum_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, '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_mean_sum_1(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    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 = 16.0
    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, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [eye, labels, pow_1, sum_1, v_norm, sub, relu, mul, sub_1, mul_1, sub_2, relu_1, mul_2, loss], Original ATen: [aten.eye, aten.index, aten.pow, aten.sum, aten.sqrt, aten.rsub, aten.relu, aten.mul, aten.sub, aten.add]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_eye_index_mul_pow_relu_rsub_sqrt_sub_sum_0.run(arg1_1, arg0_1, buf0, 64, grid=grid(64), stream=stream0)
        del arg0_1
        del arg1_1
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [sum_2, mean], Original ATen: [aten.sum, aten.mean]
        triton_per_fused_mean_sum_1.run(buf2, buf0, 1, 16, 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, ), (1, ), device='cuda:0', dtype=torch.int64)
    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)

from torch.nn import Module
import torch
import torch.nn.functional as F
import torch.utils.data
import torch.nn.functional
import torch.autograd


class MarginLoss(Module):
    '\n    ## Margin loss for class existence\n\n    A separate margin loss is used for each output capsule and the total loss is the sum of them.\n    The length of each output capsule is the probability that class is present in the input.\n\n    Loss for each output capsule or class $k$ is,\n    $$\\mathcal{L}_k = T_k \\max(0, m^{+} - \\lVert\\mathbf{v}_k\rVert)^2 +\n    \\lambda (1 - T_k) \\max(0, \\lVert\\mathbf{v}_k\rVert - m^{-})^2$$\n\n    $T_k$ is $1$ if the class $k$ is present and $0$ otherwise.\n    The first component of the loss is $0$ when the class is not present,\n    and the second component is $0$ if the class is present.\n    The $\\max(0, x)$ is used to avoid predictions going to extremes.\n    $m^{+}$ is set to be $0.9$ and $m^{-}$ to be $0.1$ in the paper.\n\n    The $\\lambda$ down-weighting is used to stop the length of all capsules from\n    falling during the initial phase of training.\n    '

    def __init__(self, *, n_labels: int, lambda_: float=0.5, m_positive:
        float=0.9, m_negative: float=0.1):
        super().__init__()
        self.m_negative = m_negative
        self.m_positive = m_positive
        self.lambda_ = lambda_
        self.n_labels = n_labels

    def forward(self, v: 'torch.Tensor', labels: 'torch.Tensor'):
        """
        `v`, $\\mathbf{v}_j$ are the squashed output capsules.
        This has shape `[batch_size, n_labels, n_features]`; that is, there is a capsule for each label.

        `labels` are the labels, and has shape `[batch_size]`.
        """
        v_norm = torch.sqrt((v ** 2).sum(dim=-1))
        labels = torch.eye(self.n_labels, device=labels.device)[labels]
        loss = labels * F.relu(self.m_positive - v_norm) + self.lambda_ * (
            1.0 - labels) * F.relu(v_norm - self.m_negative)
        return loss.sum(dim=-1).mean()


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), torch.ones([4], dtype=torch.int64)]


def get_init_inputs():
    return [[], {'n_labels': 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
from torch.nn import Module
import torch.utils.data
import torch.nn.functional
import torch.autograd
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_add_eye_index_mul_pow_relu_rsub_sqrt_sub_sum_0(in_ptr0,
    in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 4
    x0 = xindex % 4
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + 4 * x3, xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr1 + (1 + 4 * x3), xmask, eviction_policy='evict_last'
        )
    tmp17 = tl.load(in_ptr1 + (2 + 4 * x3), xmask, eviction_policy='evict_last'
        )
    tmp20 = tl.load(in_ptr1 + (3 + 4 * x3), xmask, eviction_policy='evict_last'
        )
    tmp1 = tl.full([XBLOCK], 4, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert((0 <= tmp4) & (tmp4 < 4) | ~xmask,
        'index out of bounds: 0 <= tmp4 < 4')
    tmp6 = tmp4
    tmp7 = x0
    tmp8 = tmp6 == tmp7
    tmp9 = 1.0
    tmp10 = 0.0
    tmp11 = tl.where(tmp8, tmp9, tmp10)
    tmp13 = tmp12 * tmp12
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp21 = tmp20 * tmp20
    tmp22 = tmp19 + tmp21
    tmp23 = libdevice.sqrt(tmp22)
    tmp24 = 0.9
    tmp25 = tmp24 - tmp23
    tmp26 = tl.full([1], 0, tl.int32)
    tmp27 = triton_helpers.maximum(tmp26, tmp25)
    tmp28 = tmp11 * tmp27
    tmp29 = tmp9 - tmp11
    tmp30 = 0.5
    tmp31 = tmp29 * tmp30
    tmp32 = 0.1
    tmp33 = tmp23 - tmp32
    tmp34 = triton_helpers.maximum(tmp26, tmp33)
    tmp35 = tmp31 * tmp34
    tmp36 = tmp28 + tmp35
    tl.store(out_ptr0 + x3, tmp36, xmask)


@triton.jit
def triton_per_fused_mean_sum_1(in_out_ptr0, in_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 16
    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 = 16.0
    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,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_eye_index_mul_pow_relu_rsub_sqrt_sub_sum_0[grid
            (64)](arg1_1, arg0_1, buf0, 64, XBLOCK=64, 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, 16, XBLOCK=1,
            num_warps=2, num_stages=1)
        del buf0
    return buf2,


class MarginLossNew(Module):
    '\n    ## Margin loss for class existence\n\n    A separate margin loss is used for each output capsule and the total loss is the sum of them.\n    The length of each output capsule is the probability that class is present in the input.\n\n    Loss for each output capsule or class $k$ is,\n    $$\\mathcal{L}_k = T_k \\max(0, m^{+} - \\lVert\\mathbf{v}_k\rVert)^2 +\n    \\lambda (1 - T_k) \\max(0, \\lVert\\mathbf{v}_k\rVert - m^{-})^2$$\n\n    $T_k$ is $1$ if the class $k$ is present and $0$ otherwise.\n    The first component of the loss is $0$ when the class is not present,\n    and the second component is $0$ if the class is present.\n    The $\\max(0, x)$ is used to avoid predictions going to extremes.\n    $m^{+}$ is set to be $0.9$ and $m^{-}$ to be $0.1$ in the paper.\n\n    The $\\lambda$ down-weighting is used to stop the length of all capsules from\n    falling during the initial phase of training.\n    '

    def __init__(self, *, n_labels: int, lambda_: float=0.5, m_positive:
        float=0.9, m_negative: float=0.1):
        super().__init__()
        self.m_negative = m_negative
        self.m_positive = m_positive
        self.lambda_ = lambda_
        self.n_labels = n_labels

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