import torch
import torch.nn as nn
import torch.nn.functional as F


class encoderDepth(nn.Module):

    def __init__(self):
        super(encoderDepth, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=13, out_channels=64, kernel_size
            =4, stride=2, padding=1, bias=True)
        self.gn1 = nn.GroupNorm(4, 64)
        self.conv2 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size
            =3, stride=1, padding=1, bias=True)
        self.gn2 = nn.GroupNorm(4, 64)
        self.conv3 = nn.Conv2d(in_channels=64, out_channels=256,
            kernel_size=4, stride=2, padding=1, bias=True)
        self.gn3 = nn.GroupNorm(16, 256)
        self.conv4 = nn.Conv2d(in_channels=256, out_channels=256,
            kernel_size=3, stride=1, padding=1, bias=True)
        self.gn4 = nn.GroupNorm(16, 256)
        self.conv5 = nn.Conv2d(in_channels=256, out_channels=512,
            kernel_size=4, stride=2, padding=1, bias=True)
        self.gn5 = nn.GroupNorm(32, 512)
        self.conv6 = nn.Conv2d(in_channels=512, out_channels=512,
            kernel_size=3, stride=1, padding=1, bias=True)
        self.gn6 = nn.GroupNorm(32, 512)

    def forward(self, x):
        x1 = F.relu(self.gn1(self.conv1(x)), True)
        x2 = F.relu(self.gn2(self.conv2(x1)), True)
        x3 = F.relu(self.gn3(self.conv3(x2)), True)
        x4 = F.relu(self.gn4(self.conv4(x3)), True)
        x5 = F.relu(self.gn5(self.conv5(x4)), True)
        x = F.relu(self.gn6(self.conv6(x5)), True)
        return x


def get_inputs():
    return [torch.rand([4, 13, 64, 64])]


def get_init_inputs():
    return [[], {}]

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
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_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 832
    xnumel = 16
    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
    y3 = yindex
    y0 = yindex % 13
    y1 = yindex // 13
    tmp0 = tl.load(in_ptr0 + (x2 + 16 * y3), xmask & ymask, eviction_policy
        ='evict_last')
    tl.store(out_ptr0 + (y0 + 13 * x2 + 208 * y1), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 52
    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]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y3 = yindex
    y0 = yindex % 13
    y1 = yindex // 13
    tmp0 = tl.load(in_ptr0 + (x2 + 4096 * y3), ymask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 13 * x2 + 53248 * y1), tmp0, ymask)


@triton.jit
def triton_poi_fused_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 64
    y1 = yindex // 64
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 64 * x2 + 576 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 64
    y1 = yindex // 64
    tmp0 = tl.load(in_ptr0 + (x2 + 16 * y3), xmask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 64 * x2 + 1024 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_4(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)
        ) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 256
    y1 = yindex // 256
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 256 * x2 + 2304 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_5(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 16
    yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)
        ) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 256
    y1 = yindex // 256
    tmp0 = tl.load(in_ptr0 + (x2 + 16 * y3), xmask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 256 * x2 + 4096 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_6(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)
        ) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 512
    y1 = yindex // 512
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 512 * x2 + 4608 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_convolution_7(in_out_ptr0, in_ptr0, 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_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, None)


@triton.jit
def triton_per_fused_native_group_norm_8(in_ptr0, out_ptr0, out_ptr1,
    out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r3 = rindex
    x0 = xindex % 8
    x1 = xindex // 8 % 64
    x2 = xindex // 512
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (8 * x0 + 64 * ((r3 + 128 * x1) % 1024) + 
        65536 * x2 + (r3 + 128 * x1) // 1024), None, eviction_policy=
        'evict_last')
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp5 = tl.sum(tmp3, 1)[:, None]
    tmp6 = tl.full([XBLOCK, 1], 128, tl.int32)
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp5 / tmp7
    tmp9 = tmp1 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK, RBLOCK])
    tmp13 = tl.sum(tmp11, 1)[:, None]
    tl.store(out_ptr0 + x4, tmp8, None)
    tl.store(out_ptr1 + x4, tmp13, None)
    tl.store(out_ptr2 + x4, tmp7, None)


@triton.jit
def triton_per_fused_native_group_norm_9(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 32
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r2 = rindex
    x0 = xindex % 8
    x1 = xindex // 8
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 8 * r2 + 512 * x1), xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (x0 + 8 * r2 + 512 * x1), xmask, other=0.0)
    tmp2 = tl.load(in_ptr2 + (x0 + 8 * r2 + 512 * x1), xmask, other=0.0)
    tmp3 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp5 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp7 = tl.where(xmask, tmp3, 0)
    tmp8 = tl.where(xmask, tmp4, 0)
    tmp9 = tl.where(xmask, tmp5, 0)
    tmp10, tmp11, tmp12 = triton_helpers.welford(tmp7, tmp8, tmp9, 1)
    tmp13 = tmp10[:, None]
    tmp14 = tmp11[:, None]
    tmp15 = tmp12[:, None]
    tl.store(out_ptr0 + x3, tmp13, xmask)
    tl.store(out_ptr1 + x3, tmp14, xmask)
    tl.store(out_ptr2 + x3, tmp15, xmask)


@triton.jit
def triton_per_fused_native_group_norm_10(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 2
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 2 * x0), xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (r1 + 2 * x0), xmask, other=0.0)
    tmp2 = tl.load(in_ptr2 + (r1 + 2 * x0), xmask, other=0.0)
    tmp3 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp5 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp7 = tl.where(xmask, tmp3, 0)
    tmp8 = tl.where(xmask, tmp4, 0)
    tmp9 = tl.where(xmask, tmp5, 0)
    tmp10, tmp11, tmp12 = triton_helpers.welford(tmp7, tmp8, tmp9, 1)
    tmp13 = tmp10[:, None]
    tmp14 = tmp11[:, None]
    tmp12[:, None]
    tmp16 = 16384.0
    tmp17 = tmp14 / tmp16
    tmp18 = 1e-05
    tmp19 = tmp17 + tmp18
    tmp20 = libdevice.rsqrt(tmp19)
    tl.store(out_ptr2 + x0, tmp20, xmask)
    tl.store(out_ptr0 + x0, tmp13, xmask)
    tl.store(out_ptr1 + x0, tmp14, xmask)


@triton.jit
def triton_poi_fused_native_group_norm_relu_11(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x0 = xindex % 64
    x2 = xindex // 65536
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + (4 * x2 + x0 // 16), None, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr2 + (4 * x2 + x0 // 16), None, eviction_policy=
        'evict_last')
    tmp10 = tl.load(in_ptr3 + x0, None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 16384.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tl.store(out_ptr0 + x3, tmp15, None)


@triton.jit
def triton_poi_fused_convolution_12(in_out_ptr0, in_ptr0, 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_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, None)


@triton.jit
def triton_red_fused_native_group_norm_13(in_ptr0, out_ptr0, out_ptr1,
    out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
    xnumel = 64
    rnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex % 16
    x1 = xindex // 16
    tmp2_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp2_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    x4 = xindex
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex % 16
        r3 = rindex // 16
        tmp0 = tl.load(in_ptr0 + (r2 + 16 * x0 + 256 * r3 + 65536 * x1), 
            rmask & xmask, eviction_policy='evict_first', other=0.0)
        tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
        tmp2_mean_next, tmp2_m2_next, tmp2_weight_next = (triton_helpers.
            welford_reduce(tmp1, tmp2_mean, tmp2_m2, tmp2_weight, roffset == 0)
            )
        tmp2_mean = tl.where(rmask & xmask, tmp2_mean_next, tmp2_mean)
        tmp2_m2 = tl.where(rmask & xmask, tmp2_m2_next, tmp2_m2)
        tmp2_weight = tl.where(rmask & xmask, tmp2_weight_next, tmp2_weight)
    tmp2_tmp, tmp3_tmp, tmp4_tmp = triton_helpers.welford(tmp2_mean,
        tmp2_m2, tmp2_weight, 1)
    tmp2 = tmp2_tmp[:, None]
    tmp3 = tmp3_tmp[:, None]
    tmp4_tmp[:, None]
    tl.store(out_ptr0 + x4, tmp2, xmask)
    tl.store(out_ptr1 + x4, tmp3, xmask)
    tmp5 = 4096.0
    tmp6 = tmp3 / tmp5
    tmp7 = 1e-05
    tmp8 = tmp6 + tmp7
    tmp9 = libdevice.rsqrt(tmp8)
    tl.store(out_ptr2 + x4, tmp9, xmask)


@triton.jit
def triton_poi_fused_native_group_norm_relu_14(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x0 = xindex % 256
    x2 = xindex // 65536
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + (16 * x2 + x0 // 16), None, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr2 + (16 * x2 + x0 // 16), None, eviction_policy=
        'evict_last')
    tmp10 = tl.load(in_ptr3 + x0, None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 4096.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tl.store(out_ptr0 + x3, tmp15, None)


@triton.jit
def triton_poi_fused_convolution_15(in_out_ptr0, in_ptr0, 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_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, None)


@triton.jit
def triton_per_fused_native_group_norm_16(in_ptr0, out_ptr0, out_ptr1,
    out_ptr2, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r2 = rindex % 16
    r3 = rindex // 16
    x0 = xindex % 32
    x1 = xindex // 32
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (r2 + 16 * x0 + 512 * r3 + 32768 * x1), None)
    tmp1 = tl.broadcast_to(tmp0, [RBLOCK])
    tmp3 = tl.broadcast_to(tmp1, [RBLOCK])
    tmp5 = triton_helpers.promote_to_tensor(tl.sum(tmp3, 0))
    tmp6 = tl.full([1], 1024, tl.int32)
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp5 / tmp7
    tmp9 = tmp1 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tl.broadcast_to(tmp10, [RBLOCK])
    tmp13 = triton_helpers.promote_to_tensor(tl.sum(tmp11, 0))
    tmp14 = 1024.0
    tmp15 = tmp13 / tmp14
    tmp16 = 1e-05
    tmp17 = tmp15 + tmp16
    tmp18 = libdevice.rsqrt(tmp17)
    tl.store(out_ptr2 + x4, tmp18, None)
    tl.store(out_ptr0 + x4, tmp8, None)
    tl.store(out_ptr1 + x4, tmp13, None)


@triton.jit
def triton_poi_fused_native_group_norm_relu_17(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x0 = xindex % 512
    x2 = xindex // 32768
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + (32 * x2 + x0 // 16), None, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr2 + (32 * x2 + x0 // 16), None, eviction_policy=
        'evict_last')
    tmp10 = tl.load(in_ptr3 + x0, None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 1024.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tl.store(out_ptr0 + x3, tmp15, None)


@triton.jit
def triton_poi_fused_native_group_norm_relu_threshold_backward_18(in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, out_ptr1, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    xnumel = 64
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 512
    y1 = yindex // 512
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 512 * x2 + 32768 * y1), xmask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + y3 // 16, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + y3 // 16, None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + y0, None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr4 + y0, None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = 1024.0
    tmp5 = tmp3 / tmp4
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp2 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full([1, 1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tmp16 = 0.0
    tmp17 = tmp15 <= tmp16
    tl.store(out_ptr0 + (x2 + 64 * y3), tmp15, xmask)
    tl.store(out_ptr1 + (y0 + 512 * x2 + 32768 * y1), tmp17, 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) = args
    args.clear()
    assert_size_stride(primals_1, (64, 13, 4, 4), (208, 16, 4, 1))
    assert_size_stride(primals_2, (64,), (1,))
    assert_size_stride(primals_3, (4, 13, 64, 64), (53248, 4096, 64, 1))
    assert_size_stride(primals_4, (64,), (1,))
    assert_size_stride(primals_5, (64,), (1,))
    assert_size_stride(primals_6, (64, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_7, (64,), (1,))
    assert_size_stride(primals_8, (64,), (1,))
    assert_size_stride(primals_9, (64,), (1,))
    assert_size_stride(primals_10, (256, 64, 4, 4), (1024, 16, 4, 1))
    assert_size_stride(primals_11, (256,), (1,))
    assert_size_stride(primals_12, (256,), (1,))
    assert_size_stride(primals_13, (256,), (1,))
    assert_size_stride(primals_14, (256, 256, 3, 3), (2304, 9, 3, 1))
    assert_size_stride(primals_15, (256,), (1,))
    assert_size_stride(primals_16, (256,), (1,))
    assert_size_stride(primals_17, (256,), (1,))
    assert_size_stride(primals_18, (512, 256, 4, 4), (4096, 16, 4, 1))
    assert_size_stride(primals_19, (512,), (1,))
    assert_size_stride(primals_20, (512,), (1,))
    assert_size_stride(primals_21, (512,), (1,))
    assert_size_stride(primals_22, (512, 512, 3, 3), (4608, 9, 3, 1))
    assert_size_stride(primals_23, (512,), (1,))
    assert_size_stride(primals_24, (512,), (1,))
    assert_size_stride(primals_25, (512,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 13, 4, 4), (208, 1, 52, 13), torch.
            float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(832, 16)](primals_1, buf0, 832, 16, XBLOCK=
            16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((4, 13, 64, 64), (53248, 1, 832, 13),
            torch.float32)
        triton_poi_fused_1[grid(52, 4096)](primals_3, buf1, 52, 4096,
            XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((64, 64, 3, 3), (576, 1, 192, 64), torch.
            float32)
        triton_poi_fused_2[grid(4096, 9)](primals_6, buf2, 4096, 9, XBLOCK=
            16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_6
        buf3 = empty_strided_cuda((256, 64, 4, 4), (1024, 1, 256, 64),
            torch.float32)
        triton_poi_fused_3[grid(16384, 16)](primals_10, buf3, 16384, 16,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_10
        buf4 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256),
            torch.float32)
        triton_poi_fused_4[grid(65536, 9)](primals_14, buf4, 65536, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_14
        buf5 = empty_strided_cuda((512, 256, 4, 4), (4096, 1, 1024, 256),
            torch.float32)
        triton_poi_fused_5[grid(131072, 16)](primals_18, buf5, 131072, 16,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_18
        buf6 = empty_strided_cuda((512, 512, 3, 3), (4608, 1, 1536, 512),
            torch.float32)
        triton_poi_fused_6[grid(262144, 9)](primals_22, buf6, 262144, 9,
            XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_22
        buf7 = extern_kernels.convolution(buf1, buf0, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf7, (4, 64, 32, 32), (65536, 1, 2048, 64))
        buf8 = buf7
        del buf7
        triton_poi_fused_convolution_7[grid(262144)](buf8, primals_2, 
            262144, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_2
        buf9 = empty_strided_cuda((4, 4, 1, 1, 2, 64), (512, 2, 2048, 2048,
            1, 8), torch.float32)
        buf10 = empty_strided_cuda((4, 4, 1, 1, 2, 64), (512, 2, 2048, 2048,
            1, 8), torch.float32)
        buf11 = empty_strided_cuda((4, 4, 1, 1, 2, 64), (512, 2, 2048, 2048,
            1, 8), torch.float32)
        triton_per_fused_native_group_norm_8[grid(2048)](buf8, buf9, buf10,
            buf11, 2048, 128, XBLOCK=8, num_warps=8, num_stages=1)
        buf12 = empty_strided_cuda((4, 4, 1, 1, 2), (8, 2, 32, 32, 1),
            torch.float32)
        buf13 = empty_strided_cuda((4, 4, 1, 1, 2), (8, 2, 32, 32, 1),
            torch.float32)
        buf14 = empty_strided_cuda((4, 4, 1, 1, 2), (8, 2, 32, 32, 1),
            torch.float32)
        triton_per_fused_native_group_norm_9[grid(32)](buf9, buf10, buf11,
            buf12, buf13, buf14, 32, 64, XBLOCK=1, num_warps=2, num_stages=1)
        buf15 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        buf16 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        buf18 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        triton_per_fused_native_group_norm_10[grid(16)](buf12, buf13, buf14,
            buf15, buf16, buf18, 16, 2, XBLOCK=1, num_warps=2, num_stages=1)
        buf19 = empty_strided_cuda((4, 64, 32, 32), (65536, 1, 2048, 64),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_11[grid(262144)](buf8,
            buf15, buf16, primals_4, primals_5, buf19, 262144, XBLOCK=512,
            num_warps=8, num_stages=1)
        del primals_5
        buf20 = extern_kernels.convolution(buf19, buf2, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf20, (4, 64, 32, 32), (65536, 1, 2048, 64))
        buf21 = buf20
        del buf20
        triton_poi_fused_convolution_7[grid(262144)](buf21, primals_7, 
            262144, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_7
        buf22 = buf9
        del buf9
        buf23 = buf11
        del buf11
        buf24 = buf10
        del buf10
        triton_per_fused_native_group_norm_8[grid(2048)](buf21, buf22,
            buf23, buf24, 2048, 128, XBLOCK=8, num_warps=8, num_stages=1)
        buf25 = buf14
        del buf14
        buf26 = buf13
        del buf13
        buf27 = buf12
        del buf12
        triton_per_fused_native_group_norm_9[grid(32)](buf22, buf23, buf24,
            buf25, buf26, buf27, 32, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del buf22
        del buf23
        del buf24
        buf28 = buf16
        del buf16
        buf29 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        buf31 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        triton_per_fused_native_group_norm_10[grid(16)](buf25, buf26, buf27,
            buf28, buf29, buf31, 16, 2, XBLOCK=1, num_warps=2, num_stages=1)
        del buf25
        del buf26
        del buf27
        buf32 = empty_strided_cuda((4, 64, 32, 32), (65536, 1, 2048, 64),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_11[grid(262144)](buf21,
            buf28, buf29, primals_8, primals_9, buf32, 262144, XBLOCK=512,
            num_warps=8, num_stages=1)
        del buf29
        del primals_9
        buf33 = extern_kernels.convolution(buf32, buf3, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf33, (4, 256, 16, 16), (65536, 1, 4096, 256))
        buf34 = buf33
        del buf33
        triton_poi_fused_convolution_12[grid(262144)](buf34, primals_11, 
            262144, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_11
        buf35 = empty_strided_cuda((4, 16, 1, 1), (16, 1, 64, 64), torch.
            float32)
        buf36 = empty_strided_cuda((4, 16, 1, 1), (16, 1, 64, 64), torch.
            float32)
        buf38 = empty_strided_cuda((4, 16, 1, 1), (16, 1, 64, 64), torch.
            float32)
        triton_red_fused_native_group_norm_13[grid(64)](buf34, buf35, buf36,
            buf38, 64, 4096, XBLOCK=1, RBLOCK=2048, num_warps=16, num_stages=1)
        buf39 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_14[grid(262144)](buf34,
            buf35, buf36, primals_12, primals_13, buf39, 262144, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del primals_13
        buf40 = extern_kernels.convolution(buf39, buf4, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf40, (4, 256, 16, 16), (65536, 1, 4096, 256))
        buf41 = buf40
        del buf40
        triton_poi_fused_convolution_12[grid(262144)](buf41, primals_15, 
            262144, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_15
        buf42 = buf36
        del buf36
        buf43 = empty_strided_cuda((4, 16, 1, 1), (16, 1, 64, 64), torch.
            float32)
        buf45 = empty_strided_cuda((4, 16, 1, 1), (16, 1, 64, 64), torch.
            float32)
        triton_red_fused_native_group_norm_13[grid(64)](buf41, buf42, buf43,
            buf45, 64, 4096, XBLOCK=1, RBLOCK=2048, num_warps=16, num_stages=1)
        buf46 = empty_strided_cuda((4, 256, 16, 16), (65536, 1, 4096, 256),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_14[grid(262144)](buf41,
            buf42, buf43, primals_16, primals_17, buf46, 262144, XBLOCK=
            1024, num_warps=4, num_stages=1)
        del buf43
        del primals_17
        buf47 = extern_kernels.convolution(buf46, buf5, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf47, (4, 512, 8, 8), (32768, 1, 4096, 512))
        buf48 = buf47
        del buf47
        triton_poi_fused_convolution_15[grid(131072)](buf48, primals_19, 
            131072, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_19
        buf49 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        buf50 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        buf52 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        triton_per_fused_native_group_norm_16[grid(128)](buf48, buf49,
            buf50, buf52, 128, 1024, num_warps=8, num_stages=1)
        buf53 = empty_strided_cuda((4, 512, 8, 8), (32768, 1, 4096, 512),
            torch.float32)
        triton_poi_fused_native_group_norm_relu_17[grid(131072)](buf48,
            buf49, buf50, primals_20, primals_21, buf53, 131072, XBLOCK=512,
            num_warps=8, num_stages=1)
        del primals_21
        buf54 = extern_kernels.convolution(buf53, buf6, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf54, (4, 512, 8, 8), (32768, 1, 4096, 512))
        buf55 = buf54
        del buf54
        triton_poi_fused_convolution_15[grid(131072)](buf55, primals_23, 
            131072, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_23
        buf56 = buf50
        del buf50
        buf57 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        buf59 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.
            float32)
        triton_per_fused_native_group_norm_16[grid(128)](buf55, buf56,
            buf57, buf59, 128, 1024, num_warps=8, num_stages=1)
        buf60 = empty_strided_cuda((4, 512, 8, 8), (32768, 64, 8, 1), torch
            .float32)
        buf61 = empty_strided_cuda((4, 512, 8, 8), (32768, 1, 4096, 512),
            torch.bool)
        triton_poi_fused_native_group_norm_relu_threshold_backward_18[grid(
            2048, 64)](buf55, buf56, buf57, primals_24, primals_25, buf60,
            buf61, 2048, 64, XBLOCK=64, YBLOCK=16, num_warps=4, num_stages=1)
        del buf57
        del primals_25
    return (buf60, buf0, buf1, primals_4, buf2, primals_8, buf3, primals_12,
        buf4, primals_16, buf5, primals_20, buf6, primals_24, buf8,
        reinterpret_tensor(buf15, (4, 4), (4, 1), 0), reinterpret_tensor(
        buf18, (4, 4), (4, 1), 0), buf19, buf21, reinterpret_tensor(buf28,
        (4, 4), (4, 1), 0), reinterpret_tensor(buf31, (4, 4), (4, 1), 0),
        buf32, buf34, reinterpret_tensor(buf35, (4, 16), (16, 1), 0),
        reinterpret_tensor(buf38, (4, 16), (16, 1), 0), buf39, buf41,
        reinterpret_tensor(buf42, (4, 16), (16, 1), 0), reinterpret_tensor(
        buf45, (4, 16), (16, 1), 0), buf46, buf48, reinterpret_tensor(buf49,
        (4, 32), (32, 1), 0), reinterpret_tensor(buf52, (4, 32), (32, 1), 0
        ), buf53, buf55, reinterpret_tensor(buf56, (4, 32), (32, 1), 0),
        reinterpret_tensor(buf59, (4, 32), (32, 1), 0), buf61)


class encoderDepthNew(nn.Module):

    def __init__(self):
        super(encoderDepthNew, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=13, out_channels=64, kernel_size
            =4, stride=2, padding=1, bias=True)
        self.gn1 = nn.GroupNorm(4, 64)
        self.conv2 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size
            =3, stride=1, padding=1, bias=True)
        self.gn2 = nn.GroupNorm(4, 64)
        self.conv3 = nn.Conv2d(in_channels=64, out_channels=256,
            kernel_size=4, stride=2, padding=1, bias=True)
        self.gn3 = nn.GroupNorm(16, 256)
        self.conv4 = nn.Conv2d(in_channels=256, out_channels=256,
            kernel_size=3, stride=1, padding=1, bias=True)
        self.gn4 = nn.GroupNorm(16, 256)
        self.conv5 = nn.Conv2d(in_channels=256, out_channels=512,
            kernel_size=4, stride=2, padding=1, bias=True)
        self.gn5 = nn.GroupNorm(32, 512)
        self.conv6 = nn.Conv2d(in_channels=512, out_channels=512,
            kernel_size=3, stride=1, padding=1, bias=True)
        self.gn6 = nn.GroupNorm(32, 512)

    def forward(self, input_0):
        primals_1 = self.conv1.weight
        primals_2 = self.conv1.bias
        primals_4 = self.gn1.weight
        primals_5 = self.gn1.bias
        primals_6 = self.conv2.weight
        primals_7 = self.conv2.bias
        primals_8 = self.gn2.weight
        primals_9 = self.gn2.bias
        primals_10 = self.conv3.weight
        primals_11 = self.conv3.bias
        primals_12 = self.gn3.weight
        primals_13 = self.gn3.bias
        primals_14 = self.conv4.weight
        primals_15 = self.conv4.bias
        primals_16 = self.gn4.weight
        primals_17 = self.gn4.bias
        primals_18 = self.conv5.weight
        primals_19 = self.conv5.bias
        primals_20 = self.gn5.weight
        primals_21 = self.gn5.bias
        primals_22 = self.conv6.weight
        primals_23 = self.conv6.bias
        primals_24 = self.gn6.weight
        primals_25 = self.gn6.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])
        return output[0]
