import torch
import torch.nn as nn


class Model(nn.Module):

    def __init__(self, inputdim):
        super(Model, self).__init__()
        self.layer1 = nn.Linear(inputdim, 16)
        torch.nn.init.xavier_uniform_(self.layer1.weight)
        self.layer2 = nn.Linear(16, 32)
        torch.nn.init.xavier_uniform_(self.layer2.weight)
        self.layer3 = nn.Linear(32, 64)
        torch.nn.init.xavier_uniform_(self.layer3.weight)
        self.layer4 = nn.Linear(64, 128)
        torch.nn.init.xavier_uniform_(self.layer4.weight)
        self.layer5 = nn.Linear(128, 256)
        torch.nn.init.xavier_uniform_(self.layer5.weight)
        self.layer6 = nn.Linear(256, 512)
        torch.nn.init.xavier_uniform_(self.layer6.weight)
        self.layer11 = nn.Linear(512, 512)
        torch.nn.init.xavier_uniform_(self.layer11.weight)
        self.layer12 = nn.Linear(512, 512)
        torch.nn.init.xavier_uniform_(self.layer12.weight)
        self.layer13 = nn.Linear(512, 512)
        torch.nn.init.xavier_uniform_(self.layer13.weight)
        self.layer14 = nn.Linear(512, 256)
        torch.nn.init.xavier_uniform_(self.layer14.weight)
        self.layer7 = nn.Linear(256, 128)
        torch.nn.init.xavier_uniform_(self.layer7.weight)
        self.layer8 = nn.Linear(128, 64)
        torch.nn.init.xavier_uniform_(self.layer8.weight)
        self.layer9 = nn.Linear(64, 32)
        torch.nn.init.xavier_uniform_(self.layer9.weight)
        self.layer10 = nn.Linear(32, 3)
        torch.nn.init.xavier_uniform_(self.layer10.weight)

    def forward(self, motor_control):
        out = self.layer1(motor_control)
        out = nn.LeakyReLU()(out)
        out = self.layer2(out)
        out = nn.LeakyReLU()(out)
        out = self.layer3(out)
        out = nn.LeakyReLU()(out)
        out = self.layer4(out)
        out = nn.LeakyReLU()(out)
        out = self.layer5(out)
        out = nn.LeakyReLU()(out)
        out = self.layer6(out)
        out = nn.LeakyReLU()(out)
        out = self.layer11(out)
        out = nn.LeakyReLU()(out)
        out = self.layer12(out)
        out = nn.LeakyReLU()(out)
        out = self.layer13(out)
        out = nn.LeakyReLU()(out)
        out = self.layer14(out)
        out = nn.LeakyReLU()(out)
        out = self.layer7(out)
        out = nn.LeakyReLU()(out)
        out = self.layer8(out)
        out = nn.LeakyReLU()(out)
        out = self.layer9(out)
        out = nn.LeakyReLU()(out)
        out = self.layer10(out)
        out = nn.LeakyReLU()(out)
        pos_value = out
        return pos_value


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


def get_init_inputs():
    return [[], {'inputdim': 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
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_leaky_relu_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr1 + x2, tmp7, xmask)


@triton.jit
def triton_poi_fused_leaky_relu_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 32
    tmp0 = tl.load(in_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x2, tmp4, None)
    tl.store(out_ptr1 + x2, tmp7, None)


@triton.jit
def triton_poi_fused_leaky_relu_2(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x2, tmp4, None)
    tl.store(out_ptr1 + x2, tmp7, None)


@triton.jit
def triton_poi_fused_leaky_relu_3(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x2, tmp4, None)
    tl.store(out_ptr1 + x2, tmp7, None)


@triton.jit
def triton_poi_fused_leaky_relu_4(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 256
    tmp0 = tl.load(in_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x2, tmp4, None)
    tl.store(out_ptr1 + x2, tmp7, None)


@triton.jit
def triton_poi_fused_leaky_relu_5(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 512
    tmp0 = tl.load(in_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x2, tmp4, None)
    tl.store(out_ptr1 + x2, tmp7, None)


@triton.jit
def triton_poi_fused_leaky_relu_6(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 3
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.01
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr1 + x2, tmp7, 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, primals_12,
        primals_13, primals_14, primals_15, primals_16, primals_17,
        primals_18, primals_19, primals_20, primals_21, primals_22,
        primals_23, primals_24, primals_25, primals_26, primals_27,
        primals_28, primals_29) = args
    args.clear()
    assert_size_stride(primals_1, (16, 4), (4, 1))
    assert_size_stride(primals_2, (16,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (32, 16), (16, 1))
    assert_size_stride(primals_5, (32,), (1,))
    assert_size_stride(primals_6, (64, 32), (32, 1))
    assert_size_stride(primals_7, (64,), (1,))
    assert_size_stride(primals_8, (128, 64), (64, 1))
    assert_size_stride(primals_9, (128,), (1,))
    assert_size_stride(primals_10, (256, 128), (128, 1))
    assert_size_stride(primals_11, (256,), (1,))
    assert_size_stride(primals_12, (512, 256), (256, 1))
    assert_size_stride(primals_13, (512,), (1,))
    assert_size_stride(primals_14, (512, 512), (512, 1))
    assert_size_stride(primals_15, (512,), (1,))
    assert_size_stride(primals_16, (512, 512), (512, 1))
    assert_size_stride(primals_17, (512,), (1,))
    assert_size_stride(primals_18, (512, 512), (512, 1))
    assert_size_stride(primals_19, (512,), (1,))
    assert_size_stride(primals_20, (256, 512), (512, 1))
    assert_size_stride(primals_21, (256,), (1,))
    assert_size_stride(primals_22, (128, 256), (256, 1))
    assert_size_stride(primals_23, (128,), (1,))
    assert_size_stride(primals_24, (64, 128), (128, 1))
    assert_size_stride(primals_25, (64,), (1,))
    assert_size_stride(primals_26, (32, 64), (64, 1))
    assert_size_stride(primals_27, (32,), (1,))
    assert_size_stride(primals_28, (3, 32), (32, 1))
    assert_size_stride(primals_29, (3,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 16), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4, 16), (256, 64, 16, 1), torch.bool)
        buf2 = empty_strided_cuda((4, 4, 4, 16), (256, 64, 16, 1), torch.
            float32)
        get_raw_stream(0)
        triton_poi_fused_leaky_relu_0[grid(1024)](buf0, primals_2, buf1,
            buf2, 1024, XBLOCK=256, num_warps=4, num_stages=1)
        del buf0
        del primals_2
        buf3 = empty_strided_cuda((64, 32), (32, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (64, 16), (16, 1), 0),
            reinterpret_tensor(primals_4, (16, 32), (1, 16), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 4, 4, 32), (512, 128, 32, 1), torch.bool)
        buf5 = empty_strided_cuda((4, 4, 4, 32), (512, 128, 32, 1), torch.
            float32)
        triton_poi_fused_leaky_relu_1[grid(2048)](buf3, primals_5, buf4,
            buf5, 2048, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_5
        buf6 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf5, (64, 32), (32, 1), 0),
            reinterpret_tensor(primals_6, (32, 64), (1, 32), 0), out=buf6)
        buf7 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.bool
            )
        buf8 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.
            float32)
        triton_poi_fused_leaky_relu_2[grid(4096)](buf6, primals_7, buf7,
            buf8, 4096, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_7
        buf9 = empty_strided_cuda((64, 128), (128, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf8, (64, 64), (64, 1), 0),
            reinterpret_tensor(primals_8, (64, 128), (1, 64), 0), out=buf9)
        buf10 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1),
            torch.bool)
        buf11 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1),
            torch.float32)
        triton_poi_fused_leaky_relu_3[grid(8192)](buf9, primals_9, buf10,
            buf11, 8192, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_9
        buf12 = empty_strided_cuda((64, 256), (256, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf11, (64, 128), (128, 1), 0),
            reinterpret_tensor(primals_10, (128, 256), (1, 128), 0), out=buf12)
        buf13 = empty_strided_cuda((4, 4, 4, 256), (4096, 1024, 256, 1),
            torch.bool)
        buf14 = empty_strided_cuda((4, 4, 4, 256), (4096, 1024, 256, 1),
            torch.float32)
        triton_poi_fused_leaky_relu_4[grid(16384)](buf12, primals_11, buf13,
            buf14, 16384, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_11
        buf15 = empty_strided_cuda((64, 512), (512, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf14, (64, 256), (256, 1), 0),
            reinterpret_tensor(primals_12, (256, 512), (1, 256), 0), out=buf15)
        buf16 = empty_strided_cuda((4, 4, 4, 512), (8192, 2048, 512, 1),
            torch.bool)
        buf17 = empty_strided_cuda((4, 4, 4, 512), (8192, 2048, 512, 1),
            torch.float32)
        triton_poi_fused_leaky_relu_5[grid(32768)](buf15, primals_13, buf16,
            buf17, 32768, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_13
        buf18 = buf15
        del buf15
        extern_kernels.mm(reinterpret_tensor(buf17, (64, 512), (512, 1), 0),
            reinterpret_tensor(primals_14, (512, 512), (1, 512), 0), out=buf18)
        buf19 = empty_strided_cuda((4, 4, 4, 512), (8192, 2048, 512, 1),
            torch.bool)
        buf20 = empty_strided_cuda((4, 4, 4, 512), (8192, 2048, 512, 1),
            torch.float32)
        triton_poi_fused_leaky_relu_5[grid(32768)](buf18, primals_15, buf19,
            buf20, 32768, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_15
        buf21 = buf18
        del buf18
        extern_kernels.mm(reinterpret_tensor(buf20, (64, 512), (512, 1), 0),
            reinterpret_tensor(primals_16, (512, 512), (1, 512), 0), out=buf21)
        buf22 = empty_strided_cuda((4, 4, 4, 512), (8192, 2048, 512, 1),
            torch.bool)
        buf23 = empty_strided_cuda((4, 4, 4, 512), (8192, 2048, 512, 1),
            torch.float32)
        triton_poi_fused_leaky_relu_5[grid(32768)](buf21, primals_17, buf22,
            buf23, 32768, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_17
        buf24 = buf21
        del buf21
        extern_kernels.mm(reinterpret_tensor(buf23, (64, 512), (512, 1), 0),
            reinterpret_tensor(primals_18, (512, 512), (1, 512), 0), out=buf24)
        buf25 = empty_strided_cuda((4, 4, 4, 512), (8192, 2048, 512, 1),
            torch.bool)
        buf26 = empty_strided_cuda((4, 4, 4, 512), (8192, 2048, 512, 1),
            torch.float32)
        triton_poi_fused_leaky_relu_5[grid(32768)](buf24, primals_19, buf25,
            buf26, 32768, XBLOCK=256, num_warps=4, num_stages=1)
        del buf24
        del primals_19
        buf27 = buf12
        del buf12
        extern_kernels.mm(reinterpret_tensor(buf26, (64, 512), (512, 1), 0),
            reinterpret_tensor(primals_20, (512, 256), (1, 512), 0), out=buf27)
        buf28 = empty_strided_cuda((4, 4, 4, 256), (4096, 1024, 256, 1),
            torch.bool)
        buf29 = empty_strided_cuda((4, 4, 4, 256), (4096, 1024, 256, 1),
            torch.float32)
        triton_poi_fused_leaky_relu_4[grid(16384)](buf27, primals_21, buf28,
            buf29, 16384, XBLOCK=256, num_warps=4, num_stages=1)
        del buf27
        del primals_21
        buf30 = buf9
        del buf9
        extern_kernels.mm(reinterpret_tensor(buf29, (64, 256), (256, 1), 0),
            reinterpret_tensor(primals_22, (256, 128), (1, 256), 0), out=buf30)
        buf31 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1),
            torch.bool)
        buf32 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1),
            torch.float32)
        triton_poi_fused_leaky_relu_3[grid(8192)](buf30, primals_23, buf31,
            buf32, 8192, XBLOCK=256, num_warps=4, num_stages=1)
        del buf30
        del primals_23
        buf33 = buf6
        del buf6
        extern_kernels.mm(reinterpret_tensor(buf32, (64, 128), (128, 1), 0),
            reinterpret_tensor(primals_24, (128, 64), (1, 128), 0), out=buf33)
        buf34 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch
            .bool)
        buf35 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch
            .float32)
        triton_poi_fused_leaky_relu_2[grid(4096)](buf33, primals_25, buf34,
            buf35, 4096, XBLOCK=128, num_warps=4, num_stages=1)
        del buf33
        del primals_25
        buf36 = buf3
        del buf3
        extern_kernels.mm(reinterpret_tensor(buf35, (64, 64), (64, 1), 0),
            reinterpret_tensor(primals_26, (64, 32), (1, 64), 0), out=buf36)
        buf37 = empty_strided_cuda((4, 4, 4, 32), (512, 128, 32, 1), torch.bool
            )
        buf38 = empty_strided_cuda((4, 4, 4, 32), (512, 128, 32, 1), torch.
            float32)
        triton_poi_fused_leaky_relu_1[grid(2048)](buf36, primals_27, buf37,
            buf38, 2048, XBLOCK=256, num_warps=4, num_stages=1)
        del buf36
        del primals_27
        buf39 = empty_strided_cuda((64, 3), (3, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf38, (64, 32), (32, 1), 0),
            reinterpret_tensor(primals_28, (32, 3), (1, 32), 0), out=buf39)
        buf40 = empty_strided_cuda((4, 4, 4, 3), (48, 12, 3, 1), torch.bool)
        buf41 = empty_strided_cuda((4, 4, 4, 3), (48, 12, 3, 1), torch.float32)
        triton_poi_fused_leaky_relu_6[grid(192)](buf39, primals_29, buf40,
            buf41, 192, XBLOCK=128, num_warps=4, num_stages=1)
        del buf39
        del primals_29
    return (buf41, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf1,
        reinterpret_tensor(buf2, (64, 16), (16, 1), 0), buf4,
        reinterpret_tensor(buf5, (64, 32), (32, 1), 0), buf7,
        reinterpret_tensor(buf8, (64, 64), (64, 1), 0), buf10,
        reinterpret_tensor(buf11, (64, 128), (128, 1), 0), buf13,
        reinterpret_tensor(buf14, (64, 256), (256, 1), 0), buf16,
        reinterpret_tensor(buf17, (64, 512), (512, 1), 0), buf19,
        reinterpret_tensor(buf20, (64, 512), (512, 1), 0), buf22,
        reinterpret_tensor(buf23, (64, 512), (512, 1), 0), buf25,
        reinterpret_tensor(buf26, (64, 512), (512, 1), 0), buf28,
        reinterpret_tensor(buf29, (64, 256), (256, 1), 0), buf31,
        reinterpret_tensor(buf32, (64, 128), (128, 1), 0), buf34,
        reinterpret_tensor(buf35, (64, 64), (64, 1), 0), buf37,
        reinterpret_tensor(buf38, (64, 32), (32, 1), 0), buf40, primals_28,
        primals_26, primals_24, primals_22, primals_20, primals_18,
        primals_16, primals_14, primals_12, primals_10, primals_8,
        primals_6, primals_4)


class ModelNew(nn.Module):

    def __init__(self, inputdim):
        super(ModelNew, self).__init__()
        self.layer1 = nn.Linear(inputdim, 16)
        torch.nn.init.xavier_uniform_(self.layer1.weight)
        self.layer2 = nn.Linear(16, 32)
        torch.nn.init.xavier_uniform_(self.layer2.weight)
        self.layer3 = nn.Linear(32, 64)
        torch.nn.init.xavier_uniform_(self.layer3.weight)
        self.layer4 = nn.Linear(64, 128)
        torch.nn.init.xavier_uniform_(self.layer4.weight)
        self.layer5 = nn.Linear(128, 256)
        torch.nn.init.xavier_uniform_(self.layer5.weight)
        self.layer6 = nn.Linear(256, 512)
        torch.nn.init.xavier_uniform_(self.layer6.weight)
        self.layer11 = nn.Linear(512, 512)
        torch.nn.init.xavier_uniform_(self.layer11.weight)
        self.layer12 = nn.Linear(512, 512)
        torch.nn.init.xavier_uniform_(self.layer12.weight)
        self.layer13 = nn.Linear(512, 512)
        torch.nn.init.xavier_uniform_(self.layer13.weight)
        self.layer14 = nn.Linear(512, 256)
        torch.nn.init.xavier_uniform_(self.layer14.weight)
        self.layer7 = nn.Linear(256, 128)
        torch.nn.init.xavier_uniform_(self.layer7.weight)
        self.layer8 = nn.Linear(128, 64)
        torch.nn.init.xavier_uniform_(self.layer8.weight)
        self.layer9 = nn.Linear(64, 32)
        torch.nn.init.xavier_uniform_(self.layer9.weight)
        self.layer10 = nn.Linear(32, 3)
        torch.nn.init.xavier_uniform_(self.layer10.weight)

    def forward(self, input_0):
        primals_1 = self.layer1.weight
        primals_2 = self.layer1.bias
        primals_4 = self.layer2.weight
        primals_5 = self.layer2.bias
        primals_6 = self.layer3.weight
        primals_7 = self.layer3.bias
        primals_8 = self.layer4.weight
        primals_9 = self.layer4.bias
        primals_10 = self.layer5.weight
        primals_11 = self.layer5.bias
        primals_12 = self.layer6.weight
        primals_13 = self.layer6.bias
        primals_14 = self.layer11.weight
        primals_15 = self.layer11.bias
        primals_16 = self.layer12.weight
        primals_17 = self.layer12.bias
        primals_18 = self.layer13.weight
        primals_19 = self.layer13.bias
        primals_20 = self.layer14.weight
        primals_21 = self.layer14.bias
        primals_22 = self.layer7.weight
        primals_23 = self.layer7.bias
        primals_24 = self.layer8.weight
        primals_25 = self.layer8.bias
        primals_26 = self.layer9.weight
        primals_27 = self.layer9.bias
        primals_28 = self.layer10.weight
        primals_29 = self.layer10.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14,
            primals_15, primals_16, primals_17, primals_18, primals_19,
            primals_20, primals_21, primals_22, primals_23, primals_24,
            primals_25, primals_26, primals_27, primals_28, primals_29])
        return output[0]
