# 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_4/inductor_cache/xq/cxqkhhst3jbs43bo4t4kdglqlksdycss3wdyjycgxemnfciwj463.py
# Topologically Sorted Source Nodes: [x, x_1], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   x => convolution
#   x_1 => relu
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [2, 2], [3, 3], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
triton_poi_fused_convolution_relu_0 = async_compile.triton('triton_poi_fused_convolution_relu_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 4) % 4
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x3), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/5p/c5pbfru6tjxyj47hlsu4asncufntav2p4cxvrd4h3dnevr2saar2.py
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   x_2 => getitem, getitem_1
# Graph fragment:
#   %getitem : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 0), kwargs = {})
#   %getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 1), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_1 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i8', 3: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 9, '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_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.full([1], -1, tl.int64)
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 2, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = tmp5 & tmp5
    tmp7 = tl.load(in_ptr0 + ((-3) + (4*x0)), tmp6 & xmask, eviction_policy='evict_last', other=float("-inf"))
    tmp8 = tmp1 >= tmp1
    tmp9 = tmp1 < tmp3
    tmp10 = tmp8 & tmp9
    tmp11 = tmp5 & tmp10
    tmp12 = tl.load(in_ptr0 + ((-2) + (4*x0)), tmp11 & xmask, eviction_policy='evict_last', other=float("-inf"))
    tmp13 = triton_helpers.maximum(tmp12, tmp7)
    tmp14 = tl.full([1], 1, tl.int64)
    tmp15 = tmp14 >= tmp1
    tmp16 = tmp14 < tmp3
    tmp17 = tmp15 & tmp16
    tmp18 = tmp5 & tmp17
    tmp19 = tl.load(in_ptr0 + ((-1) + (4*x0)), tmp18 & xmask, eviction_policy='evict_last', other=float("-inf"))
    tmp20 = triton_helpers.maximum(tmp19, tmp13)
    tmp21 = tmp10 & tmp5
    tmp22 = tl.load(in_ptr0 + ((-1) + (4*x0)), tmp21 & xmask, eviction_policy='evict_last', other=float("-inf"))
    tmp23 = triton_helpers.maximum(tmp22, tmp20)
    tmp24 = tmp10 & tmp10
    tmp25 = tl.load(in_ptr0 + (4*x0), tmp24 & xmask, eviction_policy='evict_last', other=float("-inf"))
    tmp26 = triton_helpers.maximum(tmp25, tmp23)
    tmp27 = tmp10 & tmp17
    tmp28 = tl.load(in_ptr0 + (1 + (4*x0)), tmp27 & xmask, eviction_policy='evict_last', other=float("-inf"))
    tmp29 = triton_helpers.maximum(tmp28, tmp26)
    tmp30 = tmp17 & tmp5
    tmp31 = tl.load(in_ptr0 + (1 + (4*x0)), tmp30 & xmask, eviction_policy='evict_last', other=float("-inf"))
    tmp32 = triton_helpers.maximum(tmp31, tmp29)
    tmp33 = tmp17 & tmp10
    tmp34 = tl.load(in_ptr0 + (2 + (4*x0)), tmp33 & xmask, eviction_policy='evict_last', other=float("-inf"))
    tmp35 = triton_helpers.maximum(tmp34, tmp32)
    tmp36 = tmp17 & tmp17
    tmp37 = tl.load(in_ptr0 + (3 + (4*x0)), tmp36 & xmask, eviction_policy='evict_last', other=float("-inf"))
    tmp38 = triton_helpers.maximum(tmp37, tmp35)
    tmp39 = tmp12 > tmp7
    tmp40 = tl.full([1], 1, tl.int8)
    tmp41 = tl.full([1], 0, tl.int8)
    tmp42 = tl.where(tmp39, tmp40, tmp41)
    tmp43 = tmp19 > tmp13
    tmp44 = tl.full([1], 2, tl.int8)
    tmp45 = tl.where(tmp43, tmp44, tmp42)
    tmp46 = tmp22 > tmp20
    tmp47 = tl.full([1], 3, tl.int8)
    tmp48 = tl.where(tmp46, tmp47, tmp45)
    tmp49 = tmp25 > tmp23
    tmp50 = tl.full([1], 4, tl.int8)
    tmp51 = tl.where(tmp49, tmp50, tmp48)
    tmp52 = tmp28 > tmp26
    tmp53 = tl.full([1], 5, tl.int8)
    tmp54 = tl.where(tmp52, tmp53, tmp51)
    tmp55 = tmp31 > tmp29
    tmp56 = tl.full([1], 6, tl.int8)
    tmp57 = tl.where(tmp55, tmp56, tmp54)
    tmp58 = tmp34 > tmp32
    tmp59 = tl.full([1], 7, tl.int8)
    tmp60 = tl.where(tmp58, tmp59, tmp57)
    tmp61 = tmp37 > tmp35
    tmp62 = tl.full([1], 8, tl.int8)
    tmp63 = tl.where(tmp61, tmp62, tmp60)
    tl.store(out_ptr0 + (x0), tmp38, xmask)
    tl.store(out_ptr1 + (x0), tmp63, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 7, 7), (196, 49, 7, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(2, 2), padding=(3, 3), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 2, 2), (16, 4, 2, 1))
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [x, x_1], Original ATen: [aten.convolution, aten.relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_relu_0.run(buf1, primals_2, 64, grid=grid(64), stream=stream0)
        del primals_2
        buf2 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.int8)
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_1.run(buf1, buf2, buf3, 16, grid=grid(16), stream=stream0)
    return (buf2, primals_1, primals_3, buf1, buf3, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4, 7, 7), (196, 49, 7, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import torch
import torch.nn as nn
import torch.utils.data


class WRNConv(nn.Module):
    """
    WRN specific convolution block.

    Parameters:
    ----------
    in_channels : int
        Number of input channels.
    out_channels : int
        Number of output channels.
    kernel_size : int or tuple/list of 2 int
        Convolution window size.
    stride : int or tuple/list of 2 int
        Strides of the convolution.
    padding : int or tuple/list of 2 int
        Padding value for convolution layer.
    activate : bool
        Whether activate the convolution block.
    """

    def __init__(self, in_channels, out_channels, kernel_size, stride,
        padding, activate):
        super(WRNConv, self).__init__()
        self.activate = activate
        self.conv = nn.Conv2d(in_channels=in_channels, out_channels=
            out_channels, kernel_size=kernel_size, stride=stride, padding=
            padding, bias=True)
        if self.activate:
            self.activ = nn.ReLU(inplace=True)

    def forward(self, x):
        x = self.conv(x)
        if self.activate:
            x = self.activ(x)
        return x


class WRNInitBlock(nn.Module):
    """
    WRN specific initial block.

    Parameters:
    ----------
    in_channels : int
        Number of input channels.
    out_channels : int
        Number of output channels.
    """

    def __init__(self, in_channels, out_channels):
        super(WRNInitBlock, self).__init__()
        self.conv = WRNConv(in_channels=in_channels, out_channels=
            out_channels, kernel_size=7, stride=2, padding=3, activate=True)
        self.pool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

    def forward(self, x):
        x = self.conv(x)
        x = self.pool(x)
        return x


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


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

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
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_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, xmask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.full([1], -1, tl.int64)
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 2, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = tmp5 & tmp5
    tmp7 = tl.load(in_ptr0 + (-3 + 4 * x0), tmp6 & xmask, eviction_policy=
        'evict_last', other=float('-inf'))
    tmp8 = tmp1 >= tmp1
    tmp9 = tmp1 < tmp3
    tmp10 = tmp8 & tmp9
    tmp11 = tmp5 & tmp10
    tmp12 = tl.load(in_ptr0 + (-2 + 4 * x0), tmp11 & xmask, eviction_policy
        ='evict_last', other=float('-inf'))
    tmp13 = triton_helpers.maximum(tmp12, tmp7)
    tmp14 = tl.full([1], 1, tl.int64)
    tmp15 = tmp14 >= tmp1
    tmp16 = tmp14 < tmp3
    tmp17 = tmp15 & tmp16
    tmp18 = tmp5 & tmp17
    tmp19 = tl.load(in_ptr0 + (-1 + 4 * x0), tmp18 & xmask, eviction_policy
        ='evict_last', other=float('-inf'))
    tmp20 = triton_helpers.maximum(tmp19, tmp13)
    tmp21 = tmp10 & tmp5
    tmp22 = tl.load(in_ptr0 + (-1 + 4 * x0), tmp21 & xmask, eviction_policy
        ='evict_last', other=float('-inf'))
    tmp23 = triton_helpers.maximum(tmp22, tmp20)
    tmp24 = tmp10 & tmp10
    tmp25 = tl.load(in_ptr0 + 4 * x0, tmp24 & xmask, eviction_policy=
        'evict_last', other=float('-inf'))
    tmp26 = triton_helpers.maximum(tmp25, tmp23)
    tmp27 = tmp10 & tmp17
    tmp28 = tl.load(in_ptr0 + (1 + 4 * x0), tmp27 & xmask, eviction_policy=
        'evict_last', other=float('-inf'))
    tmp29 = triton_helpers.maximum(tmp28, tmp26)
    tmp30 = tmp17 & tmp5
    tmp31 = tl.load(in_ptr0 + (1 + 4 * x0), tmp30 & xmask, eviction_policy=
        'evict_last', other=float('-inf'))
    tmp32 = triton_helpers.maximum(tmp31, tmp29)
    tmp33 = tmp17 & tmp10
    tmp34 = tl.load(in_ptr0 + (2 + 4 * x0), tmp33 & xmask, eviction_policy=
        'evict_last', other=float('-inf'))
    tmp35 = triton_helpers.maximum(tmp34, tmp32)
    tmp36 = tmp17 & tmp17
    tmp37 = tl.load(in_ptr0 + (3 + 4 * x0), tmp36 & xmask, eviction_policy=
        'evict_last', other=float('-inf'))
    tmp38 = triton_helpers.maximum(tmp37, tmp35)
    tmp39 = tmp12 > tmp7
    tmp40 = tl.full([1], 1, tl.int8)
    tmp41 = tl.full([1], 0, tl.int8)
    tmp42 = tl.where(tmp39, tmp40, tmp41)
    tmp43 = tmp19 > tmp13
    tmp44 = tl.full([1], 2, tl.int8)
    tmp45 = tl.where(tmp43, tmp44, tmp42)
    tmp46 = tmp22 > tmp20
    tmp47 = tl.full([1], 3, tl.int8)
    tmp48 = tl.where(tmp46, tmp47, tmp45)
    tmp49 = tmp25 > tmp23
    tmp50 = tl.full([1], 4, tl.int8)
    tmp51 = tl.where(tmp49, tmp50, tmp48)
    tmp52 = tmp28 > tmp26
    tmp53 = tl.full([1], 5, tl.int8)
    tmp54 = tl.where(tmp52, tmp53, tmp51)
    tmp55 = tmp31 > tmp29
    tmp56 = tl.full([1], 6, tl.int8)
    tmp57 = tl.where(tmp55, tmp56, tmp54)
    tmp58 = tmp34 > tmp32
    tmp59 = tl.full([1], 7, tl.int8)
    tmp60 = tl.where(tmp58, tmp59, tmp57)
    tmp61 = tmp37 > tmp35
    tmp62 = tl.full([1], 8, tl.int8)
    tmp63 = tl.where(tmp61, tmp62, tmp60)
    tl.store(out_ptr0 + x0, tmp38, xmask)
    tl.store(out_ptr1 + x0, tmp63, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 7, 7), (196, 49, 7, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(2, 
            2), padding=(3, 3), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 2, 2), (16, 4, 2, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(64)](buf1, primals_2, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.int8)
        triton_poi_fused_max_pool2d_with_indices_1[grid(16)](buf1, buf2,
            buf3, 16, XBLOCK=16, num_warps=1, num_stages=1)
    return buf2, primals_1, primals_3, buf1, buf3


class WRNConv(nn.Module):
    """
    WRN specific convolution block.

    Parameters:
    ----------
    in_channels : int
        Number of input channels.
    out_channels : int
        Number of output channels.
    kernel_size : int or tuple/list of 2 int
        Convolution window size.
    stride : int or tuple/list of 2 int
        Strides of the convolution.
    padding : int or tuple/list of 2 int
        Padding value for convolution layer.
    activate : bool
        Whether activate the convolution block.
    """

    def __init__(self, in_channels, out_channels, kernel_size, stride,
        padding, activate):
        super(WRNConv, self).__init__()
        self.activate = activate
        self.conv = nn.Conv2d(in_channels=in_channels, out_channels=
            out_channels, kernel_size=kernel_size, stride=stride, padding=
            padding, bias=True)
        if self.activate:
            self.activ = nn.ReLU(inplace=True)

    def forward(self, x):
        x = self.conv(x)
        if self.activate:
            x = self.activ(x)
        return x


class WRNInitBlockNew(nn.Module):
    """
    WRN specific initial block.

    Parameters:
    ----------
    in_channels : int
        Number of input channels.
    out_channels : int
        Number of output channels.
    """

    def __init__(self, in_channels, out_channels):
        super(WRNInitBlockNew, self).__init__()
        self.conv = WRNConv(in_channels=in_channels, out_channels=
            out_channels, kernel_size=7, stride=2, padding=3, activate=True)
        self.pool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

    def forward(self, input_0):
        primals_1 = self.conv.conv.weight
        primals_2 = self.conv.conv.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
