# 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_6/inductor_cache/wt/cwt5qmtvc7yxu72iuj2iy3dboxfdnpjie7tsbva6mk7drgkwx5ae.py
# Topologically Sorted Source Nodes: [locs], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   locs => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%view, %view_1, %view_2, %view_3, %view_4, %view_5], 1), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_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=[2097152], 
    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_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 12, '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_cat_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, in_ptr8, in_ptr9, in_ptr10, in_ptr11, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1966080
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x1 = (xindex // 4) % 122880
    x0 = xindex % 4
    x2 = (xindex // 491520)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 16384, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + ((4096*((x0 + (4*x1)) % 16)) + (65536*(((x0 + (4*x1) + (65536*x2)) // 65536) % 4)) + (((x0 + (4*x1)) // 16) % 4096)), tmp4, eviction_policy='evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + ((x0 + (4*x1)) % 16), tmp4, eviction_policy='evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tmp11 = tl.full([1], 40960, tl.int64)
    tmp12 = tmp0 < tmp11
    tmp13 = tmp10 & tmp12
    tmp14 = tl.load(in_ptr2 + ((4096*((x0 + (4*((-16384) + x1))) % 24)) + (98304*(((x0 + (4*((-16384) + x1)) + (98304*x2)) // 98304) % 4)) + (((x0 + (4*((-16384) + x1))) // 24) % 4096)), tmp13, eviction_policy='evict_last', other=0.0)
    tmp15 = tl.load(in_ptr3 + ((x0 + (4*((-16384) + x1))) % 24), tmp13, eviction_policy='evict_last', other=0.0)
    tmp16 = tmp14 + tmp15
    tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
    tmp18 = tl.where(tmp13, tmp16, tmp17)
    tmp19 = tmp0 >= tmp11
    tmp20 = tl.full([1], 65536, tl.int64)
    tmp21 = tmp0 < tmp20
    tmp22 = tmp19 & tmp21
    tmp23 = tl.load(in_ptr4 + ((4096*((x0 + (4*((-40960) + x1))) % 24)) + (98304*(((x0 + (4*((-40960) + x1)) + (98304*x2)) // 98304) % 4)) + (((x0 + (4*((-40960) + x1))) // 24) % 4096)), tmp22, eviction_policy='evict_last', other=0.0)
    tmp24 = tl.load(in_ptr5 + ((x0 + (4*((-40960) + x1))) % 24), tmp22, eviction_policy='evict_last', other=0.0)
    tmp25 = tmp23 + tmp24
    tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
    tmp27 = tl.where(tmp22, tmp25, tmp26)
    tmp28 = tmp0 >= tmp20
    tmp29 = tl.full([1], 90112, tl.int64)
    tmp30 = tmp0 < tmp29
    tmp31 = tmp28 & tmp30
    tmp32 = tl.load(in_ptr6 + ((4096*((x0 + (4*((-65536) + x1))) % 24)) + (98304*(((x0 + (4*((-65536) + x1)) + (98304*x2)) // 98304) % 4)) + (((x0 + (4*((-65536) + x1))) // 24) % 4096)), tmp31, eviction_policy='evict_last', other=0.0)
    tmp33 = tl.load(in_ptr7 + ((x0 + (4*((-65536) + x1))) % 24), tmp31, eviction_policy='evict_last', other=0.0)
    tmp34 = tmp32 + tmp33
    tmp35 = tl.full(tmp34.shape, 0.0, tmp34.dtype)
    tmp36 = tl.where(tmp31, tmp34, tmp35)
    tmp37 = tmp0 >= tmp29
    tmp38 = tl.full([1], 106496, tl.int64)
    tmp39 = tmp0 < tmp38
    tmp40 = tmp37 & tmp39
    tmp41 = tl.load(in_ptr8 + ((4096*((x0 + (4*((-90112) + x1))) % 16)) + (65536*(((x0 + (4*((-90112) + x1)) + (65536*x2)) // 65536) % 4)) + (((x0 + (4*((-90112) + x1))) // 16) % 4096)), tmp40, eviction_policy='evict_last', other=0.0)
    tmp42 = tl.load(in_ptr9 + ((x0 + (4*((-90112) + x1))) % 16), tmp40, eviction_policy='evict_last', other=0.0)
    tmp43 = tmp41 + tmp42
    tmp44 = tl.full(tmp43.shape, 0.0, tmp43.dtype)
    tmp45 = tl.where(tmp40, tmp43, tmp44)
    tmp46 = tmp0 >= tmp38
    tmp47 = tl.full([1], 122880, tl.int64)
    tmp48 = tmp0 < tmp47
    tmp49 = tl.load(in_ptr10 + ((4096*((x0 + (4*((-106496) + x1))) % 16)) + (65536*(((x0 + (4*((-106496) + x1)) + (65536*x2)) // 65536) % 4)) + (((x0 + (4*((-106496) + x1))) // 16) % 4096)), tmp46, eviction_policy='evict_last', other=0.0)
    tmp50 = tl.load(in_ptr11 + ((x0 + (4*((-106496) + x1))) % 16), tmp46, eviction_policy='evict_last', other=0.0)
    tmp51 = tmp49 + tmp50
    tmp52 = tl.full(tmp51.shape, 0.0, tmp51.dtype)
    tmp53 = tl.where(tmp46, tmp51, tmp52)
    tmp54 = tl.where(tmp40, tmp45, tmp53)
    tmp55 = tl.where(tmp31, tmp36, tmp54)
    tmp56 = tl.where(tmp22, tmp27, tmp55)
    tmp57 = tl.where(tmp13, tmp18, tmp56)
    tmp58 = tl.where(tmp4, tmp9, tmp57)
    tl.store(out_ptr0 + (x3), tmp58, None)
''', 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, 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, primals_30 = args
    args.clear()
    assert_size_stride(primals_1, (4, 512, 64, 64), (2097152, 4096, 64, 1))
    assert_size_stride(primals_2, (16, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_3, (16, ), (1, ))
    assert_size_stride(primals_4, (24, 1024, 3, 3), (9216, 9, 3, 1))
    assert_size_stride(primals_5, (24, ), (1, ))
    assert_size_stride(primals_6, (4, 1024, 64, 64), (4194304, 4096, 64, 1))
    assert_size_stride(primals_7, (24, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_8, (24, ), (1, ))
    assert_size_stride(primals_9, (4, 512, 64, 64), (2097152, 4096, 64, 1))
    assert_size_stride(primals_10, (24, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_11, (24, ), (1, ))
    assert_size_stride(primals_12, (4, 256, 64, 64), (1048576, 4096, 64, 1))
    assert_size_stride(primals_13, (16, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_14, (16, ), (1, ))
    assert_size_stride(primals_15, (4, 256, 64, 64), (1048576, 4096, 64, 1))
    assert_size_stride(primals_16, (16, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_17, (16, ), (1, ))
    assert_size_stride(primals_18, (4, 256, 64, 64), (1048576, 4096, 64, 1))
    assert_size_stride(primals_19, (16, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_20, (16, ), (1, ))
    assert_size_stride(primals_21, (24, 1024, 3, 3), (9216, 9, 3, 1))
    assert_size_stride(primals_22, (24, ), (1, ))
    assert_size_stride(primals_23, (24, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_24, (24, ), (1, ))
    assert_size_stride(primals_25, (24, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_26, (24, ), (1, ))
    assert_size_stride(primals_27, (16, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_28, (16, ), (1, ))
    assert_size_stride(primals_29, (16, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_30, (16, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [l_conv4_3], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 16, 64, 64), (65536, 4096, 64, 1))
        # Topologically Sorted Source Nodes: [l_conv7], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(primals_6, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 24, 64, 64), (98304, 4096, 64, 1))
        # Topologically Sorted Source Nodes: [l_conv8_2], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(primals_9, primals_7, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 24, 64, 64), (98304, 4096, 64, 1))
        # Topologically Sorted Source Nodes: [l_conv9_2], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(primals_12, primals_10, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 24, 64, 64), (98304, 4096, 64, 1))
        # Topologically Sorted Source Nodes: [l_conv10_2], Original ATen: [aten.convolution]
        buf4 = extern_kernels.convolution(primals_15, primals_13, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 16, 64, 64), (65536, 4096, 64, 1))
        # Topologically Sorted Source Nodes: [l_conv11_2], Original ATen: [aten.convolution]
        buf5 = extern_kernels.convolution(primals_18, primals_16, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 16, 64, 64), (65536, 4096, 64, 1))
        # Topologically Sorted Source Nodes: [c_conv4_3], Original ATen: [aten.convolution]
        buf6 = extern_kernels.convolution(primals_1, primals_19, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 16, 64, 64), (65536, 4096, 64, 1))
        # Topologically Sorted Source Nodes: [c_conv7], Original ATen: [aten.convolution]
        buf7 = extern_kernels.convolution(primals_6, primals_21, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf7, (4, 24, 64, 64), (98304, 4096, 64, 1))
        # Topologically Sorted Source Nodes: [c_conv8_2], Original ATen: [aten.convolution]
        buf8 = extern_kernels.convolution(primals_9, primals_23, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 24, 64, 64), (98304, 4096, 64, 1))
        # Topologically Sorted Source Nodes: [c_conv9_2], Original ATen: [aten.convolution]
        buf9 = extern_kernels.convolution(primals_12, primals_25, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf9, (4, 24, 64, 64), (98304, 4096, 64, 1))
        # Topologically Sorted Source Nodes: [c_conv10_2], Original ATen: [aten.convolution]
        buf10 = extern_kernels.convolution(primals_15, primals_27, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 16, 64, 64), (65536, 4096, 64, 1))
        # Topologically Sorted Source Nodes: [c_conv11_2], Original ATen: [aten.convolution]
        buf11 = extern_kernels.convolution(primals_18, primals_29, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf11, (4, 16, 64, 64), (65536, 4096, 64, 1))
        buf12 = empty_strided_cuda((4, 122880, 4), (491520, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [locs], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(buf0, primals_3, buf1, primals_5, buf2, primals_8, buf3, primals_11, buf4, primals_14, buf5, primals_17, buf12, 1966080, grid=grid(1966080), stream=stream0)
        del buf0
        del buf1
        del buf2
        del buf3
        del buf4
        del buf5
        del primals_11
        del primals_14
        del primals_17
        del primals_3
        del primals_5
        del primals_8
        buf13 = empty_strided_cuda((4, 122880, 4), (491520, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [classes_scores], Original ATen: [aten.cat]
        triton_poi_fused_cat_0.run(buf6, primals_20, buf7, primals_22, buf8, primals_24, buf9, primals_26, buf10, primals_28, buf11, primals_30, buf13, 1966080, grid=grid(1966080), stream=stream0)
        del buf10
        del buf11
        del buf6
        del buf7
        del buf8
        del buf9
        del primals_20
        del primals_22
        del primals_24
        del primals_26
        del primals_28
        del primals_30
    return (buf12, buf13, primals_1, primals_2, primals_4, primals_6, primals_7, primals_9, primals_10, primals_12, primals_13, primals_15, primals_16, primals_18, primals_19, primals_21, primals_23, primals_25, primals_27, primals_29, )


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, 512, 64, 64), (2097152, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((16, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((24, 1024, 3, 3), (9216, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((24, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 1024, 64, 64), (4194304, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((24, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((24, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, 512, 64, 64), (2097152, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((24, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((24, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((4, 256, 64, 64), (1048576, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((16, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_15 = rand_strided((4, 256, 64, 64), (1048576, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_16 = rand_strided((16, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_17 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_18 = rand_strided((4, 256, 64, 64), (1048576, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_19 = rand_strided((16, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_20 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_21 = rand_strided((24, 1024, 3, 3), (9216, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_22 = rand_strided((24, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_23 = rand_strided((24, 512, 3, 3), (4608, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_24 = rand_strided((24, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_25 = rand_strided((24, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_26 = rand_strided((24, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_27 = rand_strided((16, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_28 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_29 = rand_strided((16, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_30 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: 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, primals_30])
    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
from torch import nn
from itertools import product as product
import torch.optim
import torch.utils.data


class PredictionConvolutions(nn.Module):
    """
    Convolutions to predict class scores and bounding boxes using lower and higher-level feature maps.

    The bounding boxes (locations) are predicted as encoded offsets w.r.t each of the 8732 prior (default) boxes.
    See 'cxcy_to_gcxgcy' in utils.py for the encoding definition.

    The class scores represent the scores of each object class in each of the 8732 bounding boxes located.
    A high score for 'background' = no object.
    """

    def __init__(self, n_classes):
        """
        :param n_classes: number of different types of objects
        """
        super(PredictionConvolutions, self).__init__()
        self.n_classes = n_classes
        n_boxes = {'conv4_3': 4, 'conv7': 6, 'conv8_2': 6, 'conv9_2': 6,
            'conv10_2': 4, 'conv11_2': 4}
        self.loc_conv4_3 = nn.Conv2d(512, n_boxes['conv4_3'] * 4,
            kernel_size=3, padding=1)
        self.loc_conv7 = nn.Conv2d(1024, n_boxes['conv7'] * 4, kernel_size=
            3, padding=1)
        self.loc_conv8_2 = nn.Conv2d(512, n_boxes['conv8_2'] * 4,
            kernel_size=3, padding=1)
        self.loc_conv9_2 = nn.Conv2d(256, n_boxes['conv9_2'] * 4,
            kernel_size=3, padding=1)
        self.loc_conv10_2 = nn.Conv2d(256, n_boxes['conv10_2'] * 4,
            kernel_size=3, padding=1)
        self.loc_conv11_2 = nn.Conv2d(256, n_boxes['conv11_2'] * 4,
            kernel_size=3, padding=1)
        self.cl_conv4_3 = nn.Conv2d(512, n_boxes['conv4_3'] * n_classes,
            kernel_size=3, padding=1)
        self.cl_conv7 = nn.Conv2d(1024, n_boxes['conv7'] * n_classes,
            kernel_size=3, padding=1)
        self.cl_conv8_2 = nn.Conv2d(512, n_boxes['conv8_2'] * n_classes,
            kernel_size=3, padding=1)
        self.cl_conv9_2 = nn.Conv2d(256, n_boxes['conv9_2'] * n_classes,
            kernel_size=3, padding=1)
        self.cl_conv10_2 = nn.Conv2d(256, n_boxes['conv10_2'] * n_classes,
            kernel_size=3, padding=1)
        self.cl_conv11_2 = nn.Conv2d(256, n_boxes['conv11_2'] * n_classes,
            kernel_size=3, padding=1)
        self.init_conv2d()

    def init_conv2d(self):
        """
        Initialize convolution parameters.
        """
        for c in self.children():
            if isinstance(c, nn.Conv2d):
                nn.init.xavier_uniform_(c.weight)
                nn.init.constant_(c.bias, 0.0)

    def forward(self, conv4_3_feats, conv7_feats, conv8_2_feats,
        conv9_2_feats, conv10_2_feats, conv11_2_feats):
        """
        Forward propagation.

        :param conv4_3_feats: conv4_3 feature map, a tensor of dimensions (N, 512, 38, 38)
        :param conv7_feats: conv7 feature map, a tensor of dimensions (N, 1024, 19, 19)
        :param conv8_2_feats: conv8_2 feature map, a tensor of dimensions (N, 512, 10, 10)
        :param conv9_2_feats: conv9_2 feature map, a tensor of dimensions (N, 256, 5, 5)
        :param conv10_2_feats: conv10_2 feature map, a tensor of dimensions (N, 256, 3, 3)
        :param conv11_2_feats: conv11_2 feature map, a tensor of dimensions (N, 256, 1, 1)
        :return: 8732 locations and class scores (i.e. w.r.t each prior box) for each image
        """
        batch_size = conv4_3_feats.size(0)
        l_conv4_3 = self.loc_conv4_3(conv4_3_feats)
        l_conv4_3 = l_conv4_3.permute(0, 2, 3, 1).contiguous()
        l_conv4_3 = l_conv4_3.view(batch_size, -1, 4)
        l_conv7 = self.loc_conv7(conv7_feats)
        l_conv7 = l_conv7.permute(0, 2, 3, 1).contiguous()
        l_conv7 = l_conv7.view(batch_size, -1, 4)
        l_conv8_2 = self.loc_conv8_2(conv8_2_feats)
        l_conv8_2 = l_conv8_2.permute(0, 2, 3, 1).contiguous()
        l_conv8_2 = l_conv8_2.view(batch_size, -1, 4)
        l_conv9_2 = self.loc_conv9_2(conv9_2_feats)
        l_conv9_2 = l_conv9_2.permute(0, 2, 3, 1).contiguous()
        l_conv9_2 = l_conv9_2.view(batch_size, -1, 4)
        l_conv10_2 = self.loc_conv10_2(conv10_2_feats)
        l_conv10_2 = l_conv10_2.permute(0, 2, 3, 1).contiguous()
        l_conv10_2 = l_conv10_2.view(batch_size, -1, 4)
        l_conv11_2 = self.loc_conv11_2(conv11_2_feats)
        l_conv11_2 = l_conv11_2.permute(0, 2, 3, 1).contiguous()
        l_conv11_2 = l_conv11_2.view(batch_size, -1, 4)
        c_conv4_3 = self.cl_conv4_3(conv4_3_feats)
        c_conv4_3 = c_conv4_3.permute(0, 2, 3, 1).contiguous()
        c_conv4_3 = c_conv4_3.view(batch_size, -1, self.n_classes)
        c_conv7 = self.cl_conv7(conv7_feats)
        c_conv7 = c_conv7.permute(0, 2, 3, 1).contiguous()
        c_conv7 = c_conv7.view(batch_size, -1, self.n_classes)
        c_conv8_2 = self.cl_conv8_2(conv8_2_feats)
        c_conv8_2 = c_conv8_2.permute(0, 2, 3, 1).contiguous()
        c_conv8_2 = c_conv8_2.view(batch_size, -1, self.n_classes)
        c_conv9_2 = self.cl_conv9_2(conv9_2_feats)
        c_conv9_2 = c_conv9_2.permute(0, 2, 3, 1).contiguous()
        c_conv9_2 = c_conv9_2.view(batch_size, -1, self.n_classes)
        c_conv10_2 = self.cl_conv10_2(conv10_2_feats)
        c_conv10_2 = c_conv10_2.permute(0, 2, 3, 1).contiguous()
        c_conv10_2 = c_conv10_2.view(batch_size, -1, self.n_classes)
        c_conv11_2 = self.cl_conv11_2(conv11_2_feats)
        c_conv11_2 = c_conv11_2.permute(0, 2, 3, 1).contiguous()
        c_conv11_2 = c_conv11_2.view(batch_size, -1, self.n_classes)
        locs = torch.cat([l_conv4_3, l_conv7, l_conv8_2, l_conv9_2,
            l_conv10_2, l_conv11_2], dim=1)
        classes_scores = torch.cat([c_conv4_3, c_conv7, c_conv8_2,
            c_conv9_2, c_conv10_2, c_conv11_2], dim=1)
        return locs, classes_scores


def get_inputs():
    return [torch.rand([4, 512, 64, 64]), torch.rand([4, 1024, 64, 64]),
        torch.rand([4, 512, 64, 64]), torch.rand([4, 256, 64, 64]), torch.
        rand([4, 256, 64, 64]), torch.rand([4, 256, 64, 64])]


def get_init_inputs():
    return [[], {'n_classes': 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 import nn
from itertools import product as product
import torch.optim
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_cat_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    in_ptr5, in_ptr6, in_ptr7, in_ptr8, in_ptr9, in_ptr10, in_ptr11,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x1 = xindex // 4 % 122880
    x0 = xindex % 4
    x2 = xindex // 491520
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 16384, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4096 * ((x0 + 4 * x1) % 16) + 65536 * ((x0 + 
        4 * x1 + 65536 * x2) // 65536 % 4) + (x0 + 4 * x1) // 16 % 4096),
        tmp4, eviction_policy='evict_last', other=0.0)
    tmp6 = tl.load(in_ptr1 + (x0 + 4 * x1) % 16, tmp4, eviction_policy=
        'evict_last', other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tmp11 = tl.full([1], 40960, tl.int64)
    tmp12 = tmp0 < tmp11
    tmp13 = tmp10 & tmp12
    tmp14 = tl.load(in_ptr2 + (4096 * ((x0 + 4 * (-16384 + x1)) % 24) + 
        98304 * ((x0 + 4 * (-16384 + x1) + 98304 * x2) // 98304 % 4) + (x0 +
        4 * (-16384 + x1)) // 24 % 4096), tmp13, eviction_policy=
        'evict_last', other=0.0)
    tmp15 = tl.load(in_ptr3 + (x0 + 4 * (-16384 + x1)) % 24, tmp13,
        eviction_policy='evict_last', other=0.0)
    tmp16 = tmp14 + tmp15
    tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
    tmp18 = tl.where(tmp13, tmp16, tmp17)
    tmp19 = tmp0 >= tmp11
    tmp20 = tl.full([1], 65536, tl.int64)
    tmp21 = tmp0 < tmp20
    tmp22 = tmp19 & tmp21
    tmp23 = tl.load(in_ptr4 + (4096 * ((x0 + 4 * (-40960 + x1)) % 24) + 
        98304 * ((x0 + 4 * (-40960 + x1) + 98304 * x2) // 98304 % 4) + (x0 +
        4 * (-40960 + x1)) // 24 % 4096), tmp22, eviction_policy=
        'evict_last', other=0.0)
    tmp24 = tl.load(in_ptr5 + (x0 + 4 * (-40960 + x1)) % 24, tmp22,
        eviction_policy='evict_last', other=0.0)
    tmp25 = tmp23 + tmp24
    tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
    tmp27 = tl.where(tmp22, tmp25, tmp26)
    tmp28 = tmp0 >= tmp20
    tmp29 = tl.full([1], 90112, tl.int64)
    tmp30 = tmp0 < tmp29
    tmp31 = tmp28 & tmp30
    tmp32 = tl.load(in_ptr6 + (4096 * ((x0 + 4 * (-65536 + x1)) % 24) + 
        98304 * ((x0 + 4 * (-65536 + x1) + 98304 * x2) // 98304 % 4) + (x0 +
        4 * (-65536 + x1)) // 24 % 4096), tmp31, eviction_policy=
        'evict_last', other=0.0)
    tmp33 = tl.load(in_ptr7 + (x0 + 4 * (-65536 + x1)) % 24, tmp31,
        eviction_policy='evict_last', other=0.0)
    tmp34 = tmp32 + tmp33
    tmp35 = tl.full(tmp34.shape, 0.0, tmp34.dtype)
    tmp36 = tl.where(tmp31, tmp34, tmp35)
    tmp37 = tmp0 >= tmp29
    tmp38 = tl.full([1], 106496, tl.int64)
    tmp39 = tmp0 < tmp38
    tmp40 = tmp37 & tmp39
    tmp41 = tl.load(in_ptr8 + (4096 * ((x0 + 4 * (-90112 + x1)) % 16) + 
        65536 * ((x0 + 4 * (-90112 + x1) + 65536 * x2) // 65536 % 4) + (x0 +
        4 * (-90112 + x1)) // 16 % 4096), tmp40, eviction_policy=
        'evict_last', other=0.0)
    tmp42 = tl.load(in_ptr9 + (x0 + 4 * (-90112 + x1)) % 16, tmp40,
        eviction_policy='evict_last', other=0.0)
    tmp43 = tmp41 + tmp42
    tmp44 = tl.full(tmp43.shape, 0.0, tmp43.dtype)
    tmp45 = tl.where(tmp40, tmp43, tmp44)
    tmp46 = tmp0 >= tmp38
    tl.full([1], 122880, tl.int64)
    tmp49 = tl.load(in_ptr10 + (4096 * ((x0 + 4 * (-106496 + x1)) % 16) + 
        65536 * ((x0 + 4 * (-106496 + x1) + 65536 * x2) // 65536 % 4) + (x0 +
        4 * (-106496 + x1)) // 16 % 4096), tmp46, eviction_policy=
        'evict_last', other=0.0)
    tmp50 = tl.load(in_ptr11 + (x0 + 4 * (-106496 + x1)) % 16, tmp46,
        eviction_policy='evict_last', other=0.0)
    tmp51 = tmp49 + tmp50
    tmp52 = tl.full(tmp51.shape, 0.0, tmp51.dtype)
    tmp53 = tl.where(tmp46, tmp51, tmp52)
    tmp54 = tl.where(tmp40, tmp45, tmp53)
    tmp55 = tl.where(tmp31, tmp36, tmp54)
    tmp56 = tl.where(tmp22, tmp27, tmp55)
    tmp57 = tl.where(tmp13, tmp18, tmp56)
    tmp58 = tl.where(tmp4, tmp9, tmp57)
    tl.store(out_ptr0 + x3, tmp58, None)


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, primals_30) = args
    args.clear()
    assert_size_stride(primals_1, (4, 512, 64, 64), (2097152, 4096, 64, 1))
    assert_size_stride(primals_2, (16, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_3, (16,), (1,))
    assert_size_stride(primals_4, (24, 1024, 3, 3), (9216, 9, 3, 1))
    assert_size_stride(primals_5, (24,), (1,))
    assert_size_stride(primals_6, (4, 1024, 64, 64), (4194304, 4096, 64, 1))
    assert_size_stride(primals_7, (24, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_8, (24,), (1,))
    assert_size_stride(primals_9, (4, 512, 64, 64), (2097152, 4096, 64, 1))
    assert_size_stride(primals_10, (24, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_11, (24,), (1,))
    assert_size_stride(primals_12, (4, 256, 64, 64), (1048576, 4096, 64, 1))
    assert_size_stride(primals_13, (16, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_14, (16,), (1,))
    assert_size_stride(primals_15, (4, 256, 64, 64), (1048576, 4096, 64, 1))
    assert_size_stride(primals_16, (16, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_17, (16,), (1,))
    assert_size_stride(primals_18, (4, 256, 64, 64), (1048576, 4096, 64, 1))
    assert_size_stride(primals_19, (16, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_20, (16,), (1,))
    assert_size_stride(primals_21, (24, 1024, 3, 3), (9216, 9, 3, 1))
    assert_size_stride(primals_22, (24,), (1,))
    assert_size_stride(primals_23, (24, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_24, (24,), (1,))
    assert_size_stride(primals_25, (24, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_26, (24,), (1,))
    assert_size_stride(primals_27, (16, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_28, (16,), (1,))
    assert_size_stride(primals_29, (16, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_30, (16,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 16, 64, 64), (65536, 4096, 64, 1))
        buf1 = extern_kernels.convolution(primals_6, primals_4, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 24, 64, 64), (98304, 4096, 64, 1))
        buf2 = extern_kernels.convolution(primals_9, primals_7, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 24, 64, 64), (98304, 4096, 64, 1))
        buf3 = extern_kernels.convolution(primals_12, primals_10, stride=(1,
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 24, 64, 64), (98304, 4096, 64, 1))
        buf4 = extern_kernels.convolution(primals_15, primals_13, stride=(1,
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 16, 64, 64), (65536, 4096, 64, 1))
        buf5 = extern_kernels.convolution(primals_18, primals_16, stride=(1,
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 16, 64, 64), (65536, 4096, 64, 1))
        buf6 = extern_kernels.convolution(primals_1, primals_19, stride=(1,
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 16, 64, 64), (65536, 4096, 64, 1))
        buf7 = extern_kernels.convolution(primals_6, primals_21, stride=(1,
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf7, (4, 24, 64, 64), (98304, 4096, 64, 1))
        buf8 = extern_kernels.convolution(primals_9, primals_23, stride=(1,
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 24, 64, 64), (98304, 4096, 64, 1))
        buf9 = extern_kernels.convolution(primals_12, primals_25, stride=(1,
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf9, (4, 24, 64, 64), (98304, 4096, 64, 1))
        buf10 = extern_kernels.convolution(primals_15, primals_27, stride=(
            1, 1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 16, 64, 64), (65536, 4096, 64, 1))
        buf11 = extern_kernels.convolution(primals_18, primals_29, stride=(
            1, 1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf11, (4, 16, 64, 64), (65536, 4096, 64, 1))
        buf12 = empty_strided_cuda((4, 122880, 4), (491520, 4, 1), torch.
            float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(1966080)](buf0, primals_3, buf1,
            primals_5, buf2, primals_8, buf3, primals_11, buf4, primals_14,
            buf5, primals_17, buf12, 1966080, XBLOCK=1024, num_warps=4,
            num_stages=1)
        del buf0
        del buf1
        del buf2
        del buf3
        del buf4
        del buf5
        del primals_11
        del primals_14
        del primals_17
        del primals_3
        del primals_5
        del primals_8
        buf13 = empty_strided_cuda((4, 122880, 4), (491520, 4, 1), torch.
            float32)
        triton_poi_fused_cat_0[grid(1966080)](buf6, primals_20, buf7,
            primals_22, buf8, primals_24, buf9, primals_26, buf10,
            primals_28, buf11, primals_30, buf13, 1966080, XBLOCK=1024,
            num_warps=4, num_stages=1)
        del buf10
        del buf11
        del buf6
        del buf7
        del buf8
        del buf9
        del primals_20
        del primals_22
        del primals_24
        del primals_26
        del primals_28
        del primals_30
    return (buf12, buf13, primals_1, primals_2, primals_4, primals_6,
        primals_7, primals_9, primals_10, primals_12, primals_13,
        primals_15, primals_16, primals_18, primals_19, primals_21,
        primals_23, primals_25, primals_27, primals_29)


class PredictionConvolutionsNew(nn.Module):
    """
    Convolutions to predict class scores and bounding boxes using lower and higher-level feature maps.

    The bounding boxes (locations) are predicted as encoded offsets w.r.t each of the 8732 prior (default) boxes.
    See 'cxcy_to_gcxgcy' in utils.py for the encoding definition.

    The class scores represent the scores of each object class in each of the 8732 bounding boxes located.
    A high score for 'background' = no object.
    """

    def __init__(self, n_classes):
        """
        :param n_classes: number of different types of objects
        """
        super(PredictionConvolutionsNew, self).__init__()
        self.n_classes = n_classes
        n_boxes = {'conv4_3': 4, 'conv7': 6, 'conv8_2': 6, 'conv9_2': 6,
            'conv10_2': 4, 'conv11_2': 4}
        self.loc_conv4_3 = nn.Conv2d(512, n_boxes['conv4_3'] * 4,
            kernel_size=3, padding=1)
        self.loc_conv7 = nn.Conv2d(1024, n_boxes['conv7'] * 4, kernel_size=
            3, padding=1)
        self.loc_conv8_2 = nn.Conv2d(512, n_boxes['conv8_2'] * 4,
            kernel_size=3, padding=1)
        self.loc_conv9_2 = nn.Conv2d(256, n_boxes['conv9_2'] * 4,
            kernel_size=3, padding=1)
        self.loc_conv10_2 = nn.Conv2d(256, n_boxes['conv10_2'] * 4,
            kernel_size=3, padding=1)
        self.loc_conv11_2 = nn.Conv2d(256, n_boxes['conv11_2'] * 4,
            kernel_size=3, padding=1)
        self.cl_conv4_3 = nn.Conv2d(512, n_boxes['conv4_3'] * n_classes,
            kernel_size=3, padding=1)
        self.cl_conv7 = nn.Conv2d(1024, n_boxes['conv7'] * n_classes,
            kernel_size=3, padding=1)
        self.cl_conv8_2 = nn.Conv2d(512, n_boxes['conv8_2'] * n_classes,
            kernel_size=3, padding=1)
        self.cl_conv9_2 = nn.Conv2d(256, n_boxes['conv9_2'] * n_classes,
            kernel_size=3, padding=1)
        self.cl_conv10_2 = nn.Conv2d(256, n_boxes['conv10_2'] * n_classes,
            kernel_size=3, padding=1)
        self.cl_conv11_2 = nn.Conv2d(256, n_boxes['conv11_2'] * n_classes,
            kernel_size=3, padding=1)
        self.init_conv2d()

    def init_conv2d(self):
        """
        Initialize convolution parameters.
        """
        for c in self.children():
            if isinstance(c, nn.Conv2d):
                nn.init.xavier_uniform_(c.weight)
                nn.init.constant_(c.bias, 0.0)

    def forward(self, input_0, input_1, input_2, input_3, input_4, input_5):
        primals_2 = self.loc_conv4_3.weight
        primals_3 = self.loc_conv4_3.bias
        primals_4 = self.loc_conv7.weight
        primals_5 = self.loc_conv7.bias
        primals_7 = self.loc_conv8_2.weight
        primals_8 = self.loc_conv8_2.bias
        primals_10 = self.loc_conv9_2.weight
        primals_11 = self.loc_conv9_2.bias
        primals_13 = self.loc_conv10_2.weight
        primals_14 = self.loc_conv10_2.bias
        primals_16 = self.loc_conv11_2.weight
        primals_17 = self.loc_conv11_2.bias
        primals_19 = self.cl_conv4_3.weight
        primals_20 = self.cl_conv4_3.bias
        primals_21 = self.cl_conv7.weight
        primals_22 = self.cl_conv7.bias
        primals_23 = self.cl_conv8_2.weight
        primals_24 = self.cl_conv8_2.bias
        primals_25 = self.cl_conv9_2.weight
        primals_26 = self.cl_conv9_2.bias
        primals_27 = self.cl_conv10_2.weight
        primals_28 = self.cl_conv10_2.bias
        primals_29 = self.cl_conv11_2.weight
        primals_30 = self.cl_conv11_2.bias
        primals_1 = input_0
        primals_6 = input_1
        primals_9 = input_2
        primals_12 = input_3
        primals_15 = input_4
        primals_18 = input_5
        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,
            primals_30])
        return output[0], output[1]
