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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import math
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_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 8
    x0 = xindex % 4
    x2 = xindex // 32
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 4 * x1 + 16 * x2), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 4 * (-4 + x1) + 16 * x2), tmp6 & xmask,
        other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x3, tmp10, xmask)


@triton.jit
def triton_poi_fused_clone_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 32 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp3 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp4 = tmp3 * tmp1
    tmp6 = tmp5 * tmp1
    tmp7 = triton_helpers.maximum(tmp4, tmp6)
    tmp9 = tmp8 * tmp1
    tmp10 = triton_helpers.maximum(tmp7, tmp9)
    tmp12 = tmp11 * tmp1
    tmp13 = triton_helpers.maximum(tmp10, tmp12)
    tmp14 = tmp2 - tmp13
    tmp15 = tmp14 * tmp1
    tmp16 = tl_math.exp(tmp15)
    tl.store(out_ptr0 + x2, tmp16, xmask)


@triton.jit
def triton_poi_fused__softmax_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_clone_4(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (16 + y0 + 4 * x2 + 32 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_cat_5(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 8
    x0 = xindex % 4
    x2 = xindex // 32
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x0 + 16 * x2 + x1), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (4 * x0 + 16 * x2 + (-4 + x1)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x3, tmp10, xmask)


@triton.jit
def triton_poi_fused_add_6(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
    x2 = xindex
    x0 = xindex % 16
    x1 = xindex // 16
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + 32 * x1), xmask)
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + x2, tmp2, xmask)


@triton.jit
def triton_poi_fused_add_7(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
    x2 = xindex
    x0 = xindex % 16
    x1 = xindex // 16
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + (16 + x0 + 32 * x1), xmask)
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + x2, tmp2, xmask)


@triton.jit
def triton_poi_fused_native_layer_norm_8(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp1 - tmp8
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 / tmp7
    tmp21 = 1e-08
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tl.store(out_ptr0 + x0, tmp8, xmask)
    tl.store(out_ptr1 + x0, tmp23, xmask)


@triton.jit
def triton_poi_fused_native_layer_norm_9(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    in_ptr4, 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
    x1 = xindex // 4
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (4,), (1,))
    assert_size_stride(primals_11, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 8, 4), (32, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(128)](primals_2, primals_3, buf0, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf1 = empty_strided_cuda((32, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (32, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf1)
        del primals_4
        buf2 = empty_strided_cuda((4, 8, 4), (32, 4, 1), torch.float32)
        triton_poi_fused_cat_0[grid(128)](primals_1, primals_2, buf2, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        buf3 = empty_strided_cuda((32, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (32, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf3)
        del primals_5
        buf4 = empty_strided_cuda((32, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (32, 4), (4, 1), 0),
            reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf4)
        del primals_6
        buf5 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        triton_poi_fused_clone_1[grid(16, 4)](buf1, buf5, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf6 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 4, 1), torch.float32)
        triton_poi_fused_clone_1[grid(16, 4)](buf3, buf6, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf7 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf5, (16, 4, 1), (4, 1, 0), 
            0), reinterpret_tensor(buf6, (16, 1, 4), (4, 0, 1), 0), out=buf7)
        buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_2[grid(256)](buf7, buf8, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf9 = reinterpret_tensor(buf7, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf7
        triton_poi_fused__softmax_3[grid(256)](buf8, buf9, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf10 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        triton_poi_fused_clone_1[grid(16, 4)](buf4, buf10, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf11 = empty_strided_cuda((16, 4, 1), (4, 1, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf9, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf10, (16, 4, 1), (4, 1, 0), 0), out=buf11)
        buf12 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        triton_poi_fused_clone_4[grid(16, 4)](buf1, buf12, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        del buf1
        buf13 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 4, 1), torch.float32)
        triton_poi_fused_clone_4[grid(16, 4)](buf3, buf13, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf14 = reinterpret_tensor(buf8, (16, 4, 4), (16, 4, 1), 0)
        del buf8
        extern_kernels.bmm(reinterpret_tensor(buf12, (16, 4, 1), (4, 1, 0),
            0), reinterpret_tensor(buf13, (16, 1, 4), (4, 0, 1), 0), out=buf14)
        buf15 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_2[grid(256)](buf14, buf15, 256, XBLOCK=
            256, num_warps=4, num_stages=1)
        buf16 = reinterpret_tensor(buf14, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf14
        triton_poi_fused__softmax_3[grid(256)](buf15, buf16, 256, XBLOCK=
            128, num_warps=4, num_stages=1)
        del buf15
        buf17 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        triton_poi_fused_clone_4[grid(16, 4)](buf4, buf17, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf18 = empty_strided_cuda((16, 4, 1), (4, 1, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf16, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf17, (16, 4, 1), (4, 1, 0), 0), out=buf18)
        buf19 = reinterpret_tensor(buf4, (4, 8, 4), (32, 4, 1), 0)
        del buf4
        triton_poi_fused_cat_5[grid(128)](buf11, buf18, buf19, 128, XBLOCK=
            128, num_warps=4, num_stages=1)
        buf20 = buf3
        del buf3
        extern_kernels.mm(reinterpret_tensor(buf19, (32, 4), (4, 1), 0),
            reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf20)
        buf21 = reinterpret_tensor(buf18, (4, 4, 4), (16, 4, 1), 0)
        del buf18
        triton_poi_fused_add_6[grid(64)](primals_2, buf20, buf21, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_2
        buf22 = reinterpret_tensor(buf11, (4, 4, 4), (16, 4, 1), 0)
        del buf11
        triton_poi_fused_add_7[grid(64)](primals_3, buf20, buf22, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del buf20
        del primals_3
        buf23 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf24 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        triton_poi_fused_native_layer_norm_8[grid(16)](buf21, buf23, buf24,
            16, XBLOCK=16, num_warps=1, num_stages=1)
        buf25 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_9[grid(64)](buf21, buf23, buf24,
            primals_8, primals_9, buf25, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del primals_9
        buf26 = buf24
        del buf24
        buf27 = buf23
        del buf23
        triton_poi_fused_native_layer_norm_8[grid(16)](buf22, buf26, buf27,
            16, XBLOCK=16, num_warps=1, num_stages=1)
        buf28 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_9[grid(64)](buf22, buf26, buf27,
            primals_10, primals_11, buf28, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del buf26
        del buf27
        del primals_11
    return (buf25, buf28, buf9, buf16, primals_8, primals_10,
        reinterpret_tensor(buf0, (32, 4), (4, 1), 0), reinterpret_tensor(
        buf2, (32, 4), (4, 1), 0), buf9, buf16, reinterpret_tensor(buf19, (
        32, 4), (4, 1), 0), buf21, buf22, primals_7, reinterpret_tensor(
        buf17, (16, 1, 4), (4, 1, 1), 0), reinterpret_tensor(buf12, (16, 1,
        4), (4, 1, 1), 0), reinterpret_tensor(buf13, (16, 4, 1), (4, 1, 4),
        0), reinterpret_tensor(buf10, (16, 1, 4), (4, 1, 1), 0),
        reinterpret_tensor(buf5, (16, 1, 4), (4, 1, 1), 0),
        reinterpret_tensor(buf6, (16, 4, 1), (4, 1, 4), 0))


class ScaledDotProductAttention(nn.Module):

    def __init__(self, dropout=0.0):
        super(ScaledDotProductAttention, self).__init__()
        self.dropout = nn.Dropout(dropout)

    def forward(self, query, key, value):
        assert query.size()[-1] == key.size()[-1]
        dim = query.size()[-1]
        tmp_raw_scores = torch.div(torch.matmul(query, key.transpose(-2, -1
            )), math.sqrt(dim))
        atte_weights = torch.softmax(tmp_raw_scores, dim=-1)
        atte_weights = self.dropout(atte_weights)
        output = torch.matmul(atte_weights, value)
        return output, atte_weights


class MultiHeadAttention(nn.Module):

    def __init__(self, embedding_dim, reduced_dim, n_head, dropout=0.0, eps
        =1e-08):
        super(MultiHeadAttention, self).__init__()
        assert reduced_dim % n_head == 0
        self.n_head = n_head
        self.embedding_dim = embedding_dim
        self.reduced_dim = reduced_dim
        self.Wq = nn.Linear(embedding_dim, reduced_dim, bias=False)
        self.Wk = nn.Linear(embedding_dim, reduced_dim, bias=False)
        self.Wv = nn.Linear(embedding_dim, reduced_dim, bias=False)
        self.inner_attention = ScaledDotProductAttention(dropout)
        self.Wo = nn.Linear(reduced_dim, embedding_dim, bias=False)
        self.dropout = nn.Dropout(dropout)
        self.ln = nn.LayerNorm(embedding_dim, eps=eps)

    def forward(self, query):
        residual = query
        value = key = query
        query = self.Wq(query)
        key = self.Wk(key)
        value = self.Wv(value)
        b, n, _ = query.size()
        query = query.reshape(b, n, self.n_head, self.reduced_dim // self.
            n_head)
        b, m, _ = key.size()
        key = key.reshape(b, m, self.n_head, self.reduced_dim // self.n_head)
        value = value.reshape(b, m, self.n_head, self.reduced_dim // self.
            n_head)
        query = query.transpose(1, 2)
        key = key.transpose(1, 2)
        value = value.transpose(1, 2)
        query, atte_weights = self.inner_attention(query, key, value)
        query = query.transpose(1, 2).reshape(b, n, self.reduced_dim)
        query = self.dropout(self.Wo(query))
        query = query + residual
        query = self.ln(query)
        return query, atte_weights


class ComprehensionLayer_step2New(MultiHeadAttention):

    def __init__(self, embedding_dim, reduced_dim, n_head, dropout=0.0, eps
        =1e-08):
        super(ComprehensionLayer_step2New, self).__init__(embedding_dim,
            reduced_dim, n_head, dropout)
        del self.ln
        self.mid_ln = nn.LayerNorm(embedding_dim, eps=eps)
        self.hig_ln = nn.LayerNorm(embedding_dim, eps=eps)

    def forward(self, input_0, input_1, input_2):
        primals_4 = self.Wq.weight
        primals_5 = self.Wk.weight
        primals_6 = self.Wv.weight
        primals_7 = self.Wo.weight
        primals_8 = self.mid_ln.weight
        primals_9 = self.mid_ln.bias
        primals_10 = self.hig_ln.weight
        primals_11 = self.hig_ln.bias
        primals_1 = input_0
        primals_2 = input_1
        primals_3 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11])
        return output[0], output[1], output[2], output[3]


Measured latency_ms: 0.176616