# 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/ws/cwsyrllv2dhlbraqtsqyfoykchdot6g3id5jpn6ekpusd2gqgfjh.py
# Topologically Sorted Source Nodes: [tanh, sigmoid, mul, sigmoid_1, mul_1, add, sigmoid_2, mul_2, c, sigmoid_3, tanh_1, h], Original ATen: [aten.tanh, aten.sigmoid, aten.mul, aten.add, aten.sigmoid_backward]
# Source node to ATen node mapping:
#   add => add_1
#   c => add_2
#   h => mul_3
#   mul => mul
#   mul_1 => mul_1
#   mul_2 => mul_2
#   sigmoid => sigmoid
#   sigmoid_1 => sigmoid_1
#   sigmoid_2 => sigmoid_2
#   sigmoid_3 => sigmoid_3
#   tanh => tanh
#   tanh_1 => tanh_1
# Graph fragment:
#   %tanh : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%getitem_5,), kwargs = {})
#   %sigmoid : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem_11,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%tanh, %sigmoid), kwargs = {})
#   %sigmoid_1 : [num_users=3] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem_17,), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_1, %select_2), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {})
#   %sigmoid_2 : [num_users=3] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem_23,), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_2, %select_3), kwargs = {})
#   %add_2 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_1, %mul_2), kwargs = {})
#   %sigmoid_3 : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%getitem_29,), kwargs = {})
#   %tanh_1 : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%add_2,), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_3, %tanh_1), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %sigmoid_2), kwargs = {})
#   %mul_11 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_2, %sub_2), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %sigmoid_1), kwargs = {})
#   %mul_14 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid_1, %sub_3), kwargs = {})
triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_0 = async_compile.triton('triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: '*fp32', 9: '*fp32', 10: '*fp32', 11: '*fp32', 12: '*fp32', 13: '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, 6, 7, 8, 9, 10, 11, 12, 13), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 22, '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_mul_sigmoid_sigmoid_backward_tanh_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, out_ptr0, out_ptr1, out_ptr2, out_ptr3, out_ptr4, out_ptr5, out_ptr6, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (20*x1)), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr3 + (x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (4 + x0 + (20*x1)), xmask)
    tmp9 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr2 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr3 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr0 + (8 + x0 + (20*x1)), xmask)
    tmp18 = tl.load(in_ptr1 + (8 + x0), xmask, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr2 + (8 + x0), xmask, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr3 + (8 + x0), xmask, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr4 + (16 + x2), xmask)
    tmp28 = tl.load(in_ptr0 + (12 + x0 + (20*x1)), xmask)
    tmp29 = tl.load(in_ptr1 + (12 + x0), xmask, eviction_policy='evict_last')
    tmp31 = tl.load(in_ptr2 + (12 + x0), xmask, eviction_policy='evict_last')
    tmp32 = tl.load(in_ptr3 + (12 + x0), xmask, eviction_policy='evict_last')
    tmp36 = tl.load(in_ptr5 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp44 = tl.load(in_ptr0 + (16 + x0 + (20*x1)), xmask)
    tmp45 = tl.load(in_ptr1 + (16 + x0), xmask, eviction_policy='evict_last')
    tmp47 = tl.load(in_ptr2 + (16 + x0), xmask, eviction_policy='evict_last')
    tmp48 = tl.load(in_ptr3 + (16 + x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp7 = libdevice.tanh(tmp6)
    tmp10 = tmp8 + tmp9
    tmp13 = tmp11 + tmp12
    tmp14 = tmp10 + tmp13
    tmp15 = tl.sigmoid(tmp14)
    tmp16 = tmp7 * tmp15
    tmp19 = tmp17 + tmp18
    tmp22 = tmp20 + tmp21
    tmp23 = tmp19 + tmp22
    tmp24 = tl.sigmoid(tmp23)
    tmp26 = tmp24 * tmp25
    tmp27 = tmp16 + tmp26
    tmp30 = tmp28 + tmp29
    tmp33 = tmp31 + tmp32
    tmp34 = tmp30 + tmp33
    tmp35 = tl.sigmoid(tmp34)
    tmp37 = tmp35 * tmp36
    tmp38 = tmp27 + tmp37
    tmp39 = 1.0
    tmp40 = tmp39 - tmp35
    tmp41 = tmp35 * tmp40
    tmp42 = tmp39 - tmp24
    tmp43 = tmp24 * tmp42
    tmp46 = tmp44 + tmp45
    tmp49 = tmp47 + tmp48
    tmp50 = tmp46 + tmp49
    tmp51 = tl.sigmoid(tmp50)
    tmp52 = libdevice.tanh(tmp38)
    tmp53 = tmp51 * tmp52
    tl.store(out_ptr0 + (x2), tmp7, xmask)
    tl.store(out_ptr1 + (x2), tmp15, xmask)
    tl.store(out_ptr2 + (x2), tmp38, xmask)
    tl.store(out_ptr3 + (x2), tmp41, xmask)
    tl.store(out_ptr4 + (x2), tmp43, xmask)
    tl.store(out_ptr5 + (x2), tmp51, xmask)
    tl.store(out_ptr6 + (x2), tmp53, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (20, 4), (4, 1))
    assert_size_stride(primals_3, (20, ), (1, ))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (20, 4), (4, 1))
    assert_size_stride(primals_6, (20, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 20), (20, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_1, (4, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 20), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((1, 20), (20, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_4, (1, 4), (4, 1), 0), reinterpret_tensor(primals_5, (4, 20), (1, 4), 0), out=buf1)
        del primals_5
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf8 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [tanh, sigmoid, mul, sigmoid_1, mul_1, add, sigmoid_2, mul_2, c, sigmoid_3, tanh_1, h], Original ATen: [aten.tanh, aten.sigmoid, aten.mul, aten.add, aten.sigmoid_backward]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_0.run(buf0, primals_3, buf1, primals_6, primals_1, primals_4, buf2, buf3, buf4, buf7, buf8, buf5, buf6, 16, grid=grid(16), stream=stream0)
        del buf0
        del buf1
        del primals_3
        del primals_6
    return (buf6, buf4, reinterpret_tensor(primals_1, (4, 4), (4, 1), 0), reinterpret_tensor(primals_4, (1, 4), (4, 1), 0), buf2, buf3, reinterpret_tensor(primals_1, (4, 4), (4, 1), 16), reinterpret_tensor(primals_4, (4, ), (1, ), 4), buf4, buf5, buf7, buf8, )


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), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((20, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((20, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((20, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((20, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6])
    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


class TreeLSTM(nn.Module):
    """
    Implementation of the Tree-LSTM model:
    https://arxiv.org/pdf/1503.00075.pdf
    """

    def __init__(self, num_units):
        super(TreeLSTM, self).__init__()
        self.left = nn.Linear(num_units, 5 * num_units)
        self.right = nn.Linear(num_units, 5 * num_units)

    def forward(self, left_in, right_in):
        lstm_in = self.left(left_in[0])
        lstm_in += self.right(right_in[0])
        a, i, f1, f2, o = lstm_in.chunk(5, 1)
        c = a.tanh() * i.sigmoid() + f1.sigmoid() * left_in[1] + f2.sigmoid(
            ) * right_in[1]
        h = o.sigmoid() * c.tanh()
        return h, c


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


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

import torch
from torch._inductor.select_algorithm import extern_kernels
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.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_0(in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, out_ptr0, out_ptr1,
    out_ptr2, out_ptr3, out_ptr4, out_ptr5, out_ptr6, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 20 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (4 + x0 + 20 * x1), xmask)
    tmp9 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr2 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr3 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr0 + (8 + x0 + 20 * x1), xmask)
    tmp18 = tl.load(in_ptr1 + (8 + x0), xmask, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr2 + (8 + x0), xmask, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr3 + (8 + x0), xmask, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr4 + (16 + x2), xmask)
    tmp28 = tl.load(in_ptr0 + (12 + x0 + 20 * x1), xmask)
    tmp29 = tl.load(in_ptr1 + (12 + x0), xmask, eviction_policy='evict_last')
    tmp31 = tl.load(in_ptr2 + (12 + x0), xmask, eviction_policy='evict_last')
    tmp32 = tl.load(in_ptr3 + (12 + x0), xmask, eviction_policy='evict_last')
    tmp36 = tl.load(in_ptr5 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp44 = tl.load(in_ptr0 + (16 + x0 + 20 * x1), xmask)
    tmp45 = tl.load(in_ptr1 + (16 + x0), xmask, eviction_policy='evict_last')
    tmp47 = tl.load(in_ptr2 + (16 + x0), xmask, eviction_policy='evict_last')
    tmp48 = tl.load(in_ptr3 + (16 + x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp7 = libdevice.tanh(tmp6)
    tmp10 = tmp8 + tmp9
    tmp13 = tmp11 + tmp12
    tmp14 = tmp10 + tmp13
    tmp15 = tl.sigmoid(tmp14)
    tmp16 = tmp7 * tmp15
    tmp19 = tmp17 + tmp18
    tmp22 = tmp20 + tmp21
    tmp23 = tmp19 + tmp22
    tmp24 = tl.sigmoid(tmp23)
    tmp26 = tmp24 * tmp25
    tmp27 = tmp16 + tmp26
    tmp30 = tmp28 + tmp29
    tmp33 = tmp31 + tmp32
    tmp34 = tmp30 + tmp33
    tmp35 = tl.sigmoid(tmp34)
    tmp37 = tmp35 * tmp36
    tmp38 = tmp27 + tmp37
    tmp39 = 1.0
    tmp40 = tmp39 - tmp35
    tmp41 = tmp35 * tmp40
    tmp42 = tmp39 - tmp24
    tmp43 = tmp24 * tmp42
    tmp46 = tmp44 + tmp45
    tmp49 = tmp47 + tmp48
    tmp50 = tmp46 + tmp49
    tmp51 = tl.sigmoid(tmp50)
    tmp52 = libdevice.tanh(tmp38)
    tmp53 = tmp51 * tmp52
    tl.store(out_ptr0 + x2, tmp7, xmask)
    tl.store(out_ptr1 + x2, tmp15, xmask)
    tl.store(out_ptr2 + x2, tmp38, xmask)
    tl.store(out_ptr3 + x2, tmp41, xmask)
    tl.store(out_ptr4 + x2, tmp43, xmask)
    tl.store(out_ptr5 + x2, tmp51, xmask)
    tl.store(out_ptr6 + x2, tmp53, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (20, 4), (4, 1))
    assert_size_stride(primals_3, (20,), (1,))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (20, 4), (4, 1))
    assert_size_stride(primals_6, (20,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 20), (20, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (4, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 20), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((1, 20), (20, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_4, (1, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 20), (1, 4), 0), out=buf1)
        del primals_5
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf8 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_mul_sigmoid_sigmoid_backward_tanh_0[grid(16)](buf0
            , primals_3, buf1, primals_6, primals_1, primals_4, buf2, buf3,
            buf4, buf7, buf8, buf5, buf6, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        del buf0
        del buf1
        del primals_3
        del primals_6
    return buf6, buf4, reinterpret_tensor(primals_1, (4, 4), (4, 1), 0
        ), reinterpret_tensor(primals_4, (1, 4), (4, 1), 0
        ), buf2, buf3, reinterpret_tensor(primals_1, (4, 4), (4, 1), 16
        ), reinterpret_tensor(primals_4, (4,), (1,), 4), buf4, buf5, buf7, buf8


class TreeLSTMNew(nn.Module):
    """
    Implementation of the Tree-LSTM model:
    https://arxiv.org/pdf/1503.00075.pdf
    """

    def __init__(self, num_units):
        super(TreeLSTMNew, self).__init__()
        self.left = nn.Linear(num_units, 5 * num_units)
        self.right = nn.Linear(num_units, 5 * num_units)

    def forward(self, input_0, input_1):
        primals_2 = self.left.weight
        primals_3 = self.left.bias
        primals_5 = self.right.weight
        primals_6 = self.right.bias
        primals_1 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0], output[1]
