# AOT ID: ['0_inference']
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/ig/cig47vuhapdutooboct76wzrymtxcbuu7dlxiavnyagc5buk35ye.py
# Topologically Sorted Source Nodes: [max_4, max_5], Original ATen: [aten.max]
# Source node to ATen node mapping:
#   max_4 => max_4
#   max_5 => max_5
# Graph fragment:
#   %max_4 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%arg0_1, 1), kwargs = {})
#   %max_5 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%getitem_6, 1), kwargs = {})
triton_poi_fused_max_0 = async_compile.triton('triton_poi_fused_max_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=[16], 
    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_max_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 16, '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_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (64*x1)), xmask)
    tmp1 = tl.load(in_ptr0 + (16 + x0 + (64*x1)), xmask)
    tmp3 = tl.load(in_ptr0 + (32 + x0 + (64*x1)), xmask)
    tmp5 = tl.load(in_ptr0 + (48 + x0 + (64*x1)), xmask)
    tmp7 = tl.load(in_ptr0 + (4 + x0 + (64*x1)), xmask)
    tmp8 = tl.load(in_ptr0 + (20 + x0 + (64*x1)), xmask)
    tmp10 = tl.load(in_ptr0 + (36 + x0 + (64*x1)), xmask)
    tmp12 = tl.load(in_ptr0 + (52 + x0 + (64*x1)), xmask)
    tmp15 = tl.load(in_ptr0 + (8 + x0 + (64*x1)), xmask)
    tmp16 = tl.load(in_ptr0 + (24 + x0 + (64*x1)), xmask)
    tmp18 = tl.load(in_ptr0 + (40 + x0 + (64*x1)), xmask)
    tmp20 = tl.load(in_ptr0 + (56 + x0 + (64*x1)), xmask)
    tmp23 = tl.load(in_ptr0 + (12 + x0 + (64*x1)), xmask)
    tmp24 = tl.load(in_ptr0 + (28 + x0 + (64*x1)), xmask)
    tmp26 = tl.load(in_ptr0 + (44 + x0 + (64*x1)), xmask)
    tmp28 = tl.load(in_ptr0 + (60 + x0 + (64*x1)), xmask)
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp6 = triton_helpers.maximum(tmp4, tmp5)
    tmp9 = triton_helpers.maximum(tmp7, tmp8)
    tmp11 = triton_helpers.maximum(tmp9, tmp10)
    tmp13 = triton_helpers.maximum(tmp11, tmp12)
    tmp14 = triton_helpers.maximum(tmp6, tmp13)
    tmp17 = triton_helpers.maximum(tmp15, tmp16)
    tmp19 = triton_helpers.maximum(tmp17, tmp18)
    tmp21 = triton_helpers.maximum(tmp19, tmp20)
    tmp22 = triton_helpers.maximum(tmp14, tmp21)
    tmp25 = triton_helpers.maximum(tmp23, tmp24)
    tmp27 = triton_helpers.maximum(tmp25, tmp26)
    tmp29 = triton_helpers.maximum(tmp27, tmp28)
    tmp30 = triton_helpers.maximum(tmp22, tmp29)
    tl.store(out_ptr0 + (x2), tmp30, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/kz/ckzd2zqltdogl55ubrceqzldx2tdkuzsxdgtqqc5o3e6elue77g2.py
# Topologically Sorted Source Nodes: [output], Original ATen: [aten.stack]
# Source node to ATen node mapping:
#   output => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%permute, %expand, %getitem_2, %getitem_4, %expand_1], 2), kwargs = {})
triton_poi_fused_stack_1 = async_compile.triton('triton_poi_fused_stack_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=[512], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 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_stack_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 14, '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_stack_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 320
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 20
    x1 = (xindex // 20) % 4
    x2 = (xindex // 80)
    x3 = (xindex // 20)
    x4 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + ((20*x1) + (64*x2) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr0 + ((64*x2) + ((-4) + x0)), tmp9 & xmask, eviction_policy='evict_last', other=0.0)
    tmp11 = tl.load(in_ptr0 + (20 + (64*x2) + ((-4) + x0)), tmp9 & xmask, eviction_policy='evict_last', other=0.0)
    tmp12 = triton_helpers.maximum(tmp10, tmp11)
    tmp13 = tl.load(in_ptr0 + (40 + (64*x2) + ((-4) + x0)), tmp9 & xmask, eviction_policy='evict_last', other=0.0)
    tmp14 = triton_helpers.maximum(tmp12, tmp13)
    tmp15 = tl.load(in_ptr0 + (60 + (64*x2) + ((-4) + x0)), tmp9 & xmask, eviction_policy='evict_last', other=0.0)
    tmp16 = triton_helpers.maximum(tmp14, tmp15)
    tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
    tmp18 = tl.where(tmp9, tmp16, tmp17)
    tmp19 = tmp0 >= tmp7
    tmp20 = tl.full([1], 12, tl.int64)
    tmp21 = tmp0 < tmp20
    tmp22 = tmp19 & tmp21
    tmp23 = tl.load(in_ptr0 + ((16*x3) + ((-8) + x0)), tmp22 & xmask, eviction_policy='evict_last', other=0.0)
    tmp24 = tl.load(in_ptr0 + (4 + (16*x3) + ((-8) + x0)), tmp22 & xmask, eviction_policy='evict_last', other=0.0)
    tmp25 = triton_helpers.maximum(tmp23, tmp24)
    tmp26 = tl.load(in_ptr0 + (8 + (16*x3) + ((-8) + x0)), tmp22 & xmask, eviction_policy='evict_last', other=0.0)
    tmp27 = triton_helpers.maximum(tmp25, tmp26)
    tmp28 = tl.load(in_ptr0 + (12 + (16*x3) + ((-8) + x0)), tmp22 & xmask, eviction_policy='evict_last', other=0.0)
    tmp29 = triton_helpers.maximum(tmp27, tmp28)
    tmp30 = tl.full(tmp29.shape, 0.0, tmp29.dtype)
    tmp31 = tl.where(tmp22, tmp29, tmp30)
    tmp32 = tmp0 >= tmp20
    tmp33 = tl.full([1], 16, tl.int64)
    tmp34 = tmp0 < tmp33
    tmp35 = tmp32 & tmp34
    tmp36 = tl.load(in_ptr0 + ((4*x1) + (64*x2) + ((-12) + x0)), tmp35 & xmask, eviction_policy='evict_last', other=0.0)
    tmp37 = tl.load(in_ptr0 + (16 + (4*x1) + (64*x2) + ((-12) + x0)), tmp35 & xmask, eviction_policy='evict_last', other=0.0)
    tmp38 = triton_helpers.maximum(tmp36, tmp37)
    tmp39 = tl.load(in_ptr0 + (32 + (4*x1) + (64*x2) + ((-12) + x0)), tmp35 & xmask, eviction_policy='evict_last', other=0.0)
    tmp40 = triton_helpers.maximum(tmp38, tmp39)
    tmp41 = tl.load(in_ptr0 + (48 + (4*x1) + (64*x2) + ((-12) + x0)), tmp35 & xmask, eviction_policy='evict_last', other=0.0)
    tmp42 = triton_helpers.maximum(tmp40, tmp41)
    tmp43 = tl.full(tmp42.shape, 0.0, tmp42.dtype)
    tmp44 = tl.where(tmp35, tmp42, tmp43)
    tmp45 = tmp0 >= tmp33
    tmp46 = tl.full([1], 20, tl.int64)
    tmp47 = tmp0 < tmp46
    tmp48 = tl.load(in_ptr1 + ((4*x2) + ((-16) + x0)), tmp45 & xmask, eviction_policy='evict_last', other=0.0)
    tmp49 = tl.where(tmp35, tmp44, tmp48)
    tmp50 = tl.where(tmp22, tmp31, tmp49)
    tmp51 = tl.where(tmp9, tmp18, tmp50)
    tmp52 = tl.where(tmp4, tmp5, tmp51)
    tl.store(out_ptr0 + (x4), tmp52, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [max_4, max_5], Original ATen: [aten.max]
        stream0 = get_raw_stream(0)
        triton_poi_fused_max_0.run(arg0_1, buf0, 16, grid=grid(16), stream=stream0)
        buf1 = empty_strided_cuda((4, 4, 20), (80, 20, 1), torch.float32)
        # Topologically Sorted Source Nodes: [output], Original ATen: [aten.stack]
        triton_poi_fused_stack_1.run(arg0_1, buf0, buf1, 320, grid=grid(320), stream=stream0)
        del arg0_1
        del buf0
    return (buf1, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1])
    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.optim
import torch.nn as nn


class Features_2_to_1(nn.Module):

    def __init__(self):
        """
        take a batch (bs, n_vertices, n_vertices, in_features)
        and returns (bs, n_vertices, basis * in_features)
        where basis = 5
        """
        super().__init__()

    def forward(self, x):
        b, n, _, in_features = x.size()
        basis = 5
        diag_part = torch.diagonal(x, dim1=1, dim2=2).permute(0, 2, 1)
        max_diag_part = torch.max(diag_part, 1)[0].unsqueeze(1)
        max_of_rows = torch.max(x, 2)[0]
        max_of_cols = torch.max(x, 1)[0]
        max_all = torch.max(torch.max(x, 1)[0], 1)[0].unsqueeze(1)
        op1 = diag_part
        op2 = max_diag_part.expand_as(op1)
        op3 = max_of_rows
        op4 = max_of_cols
        op5 = max_all.expand_as(op1)
        output = torch.stack([op1, op2, op3, op4, op5], dim=2)
        assert output.size() == (b, n, basis, in_features), output.size()
        return output.view(b, n, basis * in_features)


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


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

import torch
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.optim
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


@triton.jit
def triton_poi_fused_max_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
    tmp1 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
    tmp3 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tmp5 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
    tmp7 = tl.load(in_ptr0 + (4 + x0 + 64 * x1), xmask)
    tmp8 = tl.load(in_ptr0 + (20 + x0 + 64 * x1), xmask)
    tmp10 = tl.load(in_ptr0 + (36 + x0 + 64 * x1), xmask)
    tmp12 = tl.load(in_ptr0 + (52 + x0 + 64 * x1), xmask)
    tmp15 = tl.load(in_ptr0 + (8 + x0 + 64 * x1), xmask)
    tmp16 = tl.load(in_ptr0 + (24 + x0 + 64 * x1), xmask)
    tmp18 = tl.load(in_ptr0 + (40 + x0 + 64 * x1), xmask)
    tmp20 = tl.load(in_ptr0 + (56 + x0 + 64 * x1), xmask)
    tmp23 = tl.load(in_ptr0 + (12 + x0 + 64 * x1), xmask)
    tmp24 = tl.load(in_ptr0 + (28 + x0 + 64 * x1), xmask)
    tmp26 = tl.load(in_ptr0 + (44 + x0 + 64 * x1), xmask)
    tmp28 = tl.load(in_ptr0 + (60 + x0 + 64 * x1), xmask)
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp6 = triton_helpers.maximum(tmp4, tmp5)
    tmp9 = triton_helpers.maximum(tmp7, tmp8)
    tmp11 = triton_helpers.maximum(tmp9, tmp10)
    tmp13 = triton_helpers.maximum(tmp11, tmp12)
    tmp14 = triton_helpers.maximum(tmp6, tmp13)
    tmp17 = triton_helpers.maximum(tmp15, tmp16)
    tmp19 = triton_helpers.maximum(tmp17, tmp18)
    tmp21 = triton_helpers.maximum(tmp19, tmp20)
    tmp22 = triton_helpers.maximum(tmp14, tmp21)
    tmp25 = triton_helpers.maximum(tmp23, tmp24)
    tmp27 = triton_helpers.maximum(tmp25, tmp26)
    tmp29 = triton_helpers.maximum(tmp27, tmp28)
    tmp30 = triton_helpers.maximum(tmp22, tmp29)
    tl.store(out_ptr0 + x2, tmp30, xmask)


@triton.jit
def triton_poi_fused_stack_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 320
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 20
    x1 = xindex // 20 % 4
    x2 = xindex // 80
    x3 = xindex // 20
    x4 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (20 * x1 + 64 * x2 + x0), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr0 + (64 * x2 + (-4 + x0)), tmp9 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp11 = tl.load(in_ptr0 + (20 + 64 * x2 + (-4 + x0)), tmp9 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp12 = triton_helpers.maximum(tmp10, tmp11)
    tmp13 = tl.load(in_ptr0 + (40 + 64 * x2 + (-4 + x0)), tmp9 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp14 = triton_helpers.maximum(tmp12, tmp13)
    tmp15 = tl.load(in_ptr0 + (60 + 64 * x2 + (-4 + x0)), tmp9 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp16 = triton_helpers.maximum(tmp14, tmp15)
    tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
    tmp18 = tl.where(tmp9, tmp16, tmp17)
    tmp19 = tmp0 >= tmp7
    tmp20 = tl.full([1], 12, tl.int64)
    tmp21 = tmp0 < tmp20
    tmp22 = tmp19 & tmp21
    tmp23 = tl.load(in_ptr0 + (16 * x3 + (-8 + x0)), tmp22 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp24 = tl.load(in_ptr0 + (4 + 16 * x3 + (-8 + x0)), tmp22 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp25 = triton_helpers.maximum(tmp23, tmp24)
    tmp26 = tl.load(in_ptr0 + (8 + 16 * x3 + (-8 + x0)), tmp22 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp27 = triton_helpers.maximum(tmp25, tmp26)
    tmp28 = tl.load(in_ptr0 + (12 + 16 * x3 + (-8 + x0)), tmp22 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp29 = triton_helpers.maximum(tmp27, tmp28)
    tmp30 = tl.full(tmp29.shape, 0.0, tmp29.dtype)
    tmp31 = tl.where(tmp22, tmp29, tmp30)
    tmp32 = tmp0 >= tmp20
    tmp33 = tl.full([1], 16, tl.int64)
    tmp34 = tmp0 < tmp33
    tmp35 = tmp32 & tmp34
    tmp36 = tl.load(in_ptr0 + (4 * x1 + 64 * x2 + (-12 + x0)), tmp35 &
        xmask, eviction_policy='evict_last', other=0.0)
    tmp37 = tl.load(in_ptr0 + (16 + 4 * x1 + 64 * x2 + (-12 + x0)), tmp35 &
        xmask, eviction_policy='evict_last', other=0.0)
    tmp38 = triton_helpers.maximum(tmp36, tmp37)
    tmp39 = tl.load(in_ptr0 + (32 + 4 * x1 + 64 * x2 + (-12 + x0)), tmp35 &
        xmask, eviction_policy='evict_last', other=0.0)
    tmp40 = triton_helpers.maximum(tmp38, tmp39)
    tmp41 = tl.load(in_ptr0 + (48 + 4 * x1 + 64 * x2 + (-12 + x0)), tmp35 &
        xmask, eviction_policy='evict_last', other=0.0)
    tmp42 = triton_helpers.maximum(tmp40, tmp41)
    tmp43 = tl.full(tmp42.shape, 0.0, tmp42.dtype)
    tmp44 = tl.where(tmp35, tmp42, tmp43)
    tmp45 = tmp0 >= tmp33
    tl.full([1], 20, tl.int64)
    tmp48 = tl.load(in_ptr1 + (4 * x2 + (-16 + x0)), tmp45 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp49 = tl.where(tmp35, tmp44, tmp48)
    tmp50 = tl.where(tmp22, tmp31, tmp49)
    tmp51 = tl.where(tmp9, tmp18, tmp50)
    tmp52 = tl.where(tmp4, tmp5, tmp51)
    tl.store(out_ptr0 + x4, tmp52, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_max_0[grid(16)](arg0_1, buf0, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((4, 4, 20), (80, 20, 1), torch.float32)
        triton_poi_fused_stack_1[grid(320)](arg0_1, buf0, buf1, 320, XBLOCK
            =128, num_warps=4, num_stages=1)
        del arg0_1
        del buf0
    return buf1,


class Features_2_to_1New(nn.Module):

    def __init__(self):
        """
        take a batch (bs, n_vertices, n_vertices, in_features)
        and returns (bs, n_vertices, basis * in_features)
        where basis = 5
        """
        super().__init__()

    def forward(self, input_0):
        arg0_1 = input_0
        output = call([arg0_1])
        return output[0]
