# 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_7/inductor_cache/ms/cms4fv3omildjvpq2cgx3it3pwz5faljuuiat4qcyrcnq3rci2ej.py
# Topologically Sorted Source Nodes: [cdist], Original ATen: [aten._euclidean_dist]
# Source node to ATen node mapping:
#   cdist => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%mul, %sum_1, %full_default], -1), kwargs = {})
triton_poi_fused__euclidean_dist_0 = async_compile.triton('triton_poi_fused__euclidean_dist_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=[512], 
    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__euclidean_dist_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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__euclidean_dist_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 6
    x1 = (xindex // 6)
    x2 = 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 + ((4*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = -2.0
    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], 5, tl.int64)
    tmp12 = tmp0 < tmp11
    tmp13 = tmp10 & tmp12
    tmp14 = tl.load(in_ptr0 + (4*x1), tmp13 & xmask, eviction_policy='evict_last', other=0.0)
    tmp15 = tmp14 * tmp14
    tmp16 = tl.load(in_ptr0 + (1 + (4*x1)), tmp13 & xmask, eviction_policy='evict_last', other=0.0)
    tmp17 = tmp16 * tmp16
    tmp18 = tmp15 + tmp17
    tmp19 = tl.load(in_ptr0 + (2 + (4*x1)), tmp13 & xmask, eviction_policy='evict_last', other=0.0)
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp22 = tl.load(in_ptr0 + (3 + (4*x1)), tmp13 & xmask, eviction_policy='evict_last', other=0.0)
    tmp23 = tmp22 * tmp22
    tmp24 = tmp21 + tmp23
    tmp25 = tl.full(tmp24.shape, 0.0, tmp24.dtype)
    tmp26 = tl.where(tmp13, tmp24, tmp25)
    tmp27 = tmp0 >= tmp11
    tmp28 = tl.full([1], 6, tl.int64)
    tmp29 = tmp0 < tmp28
    tmp30 = 1.0
    tmp31 = tl.full(tmp30.shape, 0.0, tmp30.dtype)
    tmp32 = tl.where(tmp27, tmp30, tmp31)
    tmp33 = tl.where(tmp13, tmp26, tmp32)
    tmp34 = tl.where(tmp4, tmp9, tmp33)
    tl.store(out_ptr0 + (x2), tmp34, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/lg/clgibvy6k5yoz65ryyz35bjx6twpc4zxf7hpntq6tblfnwym3ufv.py
# Topologically Sorted Source Nodes: [cdist], Original ATen: [aten._euclidean_dist]
# Source node to ATen node mapping:
#   cdist => cat_1
# Graph fragment:
#   %cat_1 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_2, %full_default_1, %sum_2], -1), kwargs = {})
triton_poi_fused__euclidean_dist_1 = async_compile.triton('triton_poi_fused__euclidean_dist_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=[32], 
    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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__euclidean_dist_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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__euclidean_dist_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 24
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 6
    x1 = (xindex // 6)
    x2 = 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 + ((4*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 5, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = 1.0
    tmp11 = tl.full(tmp10.shape, 0.0, tmp10.dtype)
    tmp12 = tl.where(tmp9, tmp10, tmp11)
    tmp13 = tmp0 >= tmp7
    tmp14 = tl.full([1], 6, tl.int64)
    tmp15 = tmp0 < tmp14
    tmp16 = tl.load(in_ptr0 + (4*x1), tmp13 & xmask, eviction_policy='evict_last', other=0.0)
    tmp17 = tmp16 * tmp16
    tmp18 = tl.load(in_ptr0 + (1 + (4*x1)), tmp13 & xmask, eviction_policy='evict_last', other=0.0)
    tmp19 = tmp18 * tmp18
    tmp20 = tmp17 + tmp19
    tmp21 = tl.load(in_ptr0 + (2 + (4*x1)), tmp13 & xmask, eviction_policy='evict_last', other=0.0)
    tmp22 = tmp21 * tmp21
    tmp23 = tmp20 + tmp22
    tmp24 = tl.load(in_ptr0 + (3 + (4*x1)), tmp13 & xmask, eviction_policy='evict_last', other=0.0)
    tmp25 = tmp24 * tmp24
    tmp26 = tmp23 + tmp25
    tmp27 = tl.full(tmp26.shape, 0.0, tmp26.dtype)
    tmp28 = tl.where(tmp13, tmp26, tmp27)
    tmp29 = tl.where(tmp9, tmp12, tmp28)
    tmp30 = tl.where(tmp4, tmp5, tmp29)
    tl.store(out_ptr0 + (x2), tmp30, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/3k/c3kmh7lsmnzdzr342q44fn2y2heu6eptfn2e7nbfojxmv35w6kyt.py
# Topologically Sorted Source Nodes: [cdist, dis_2], Original ATen: [aten._euclidean_dist, aten.pow]
# Source node to ATen node mapping:
#   cdist => clamp_min, sqrt
#   dis_2 => pow_3
# Graph fragment:
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%mm, 0), kwargs = {})
#   %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%clamp_min,), kwargs = {})
#   %pow_3 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sqrt, 2), kwargs = {})
triton_poi_fused__euclidean_dist_pow_2 = async_compile.triton('triton_poi_fused__euclidean_dist_pow_2', '''
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=[256], 
    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__euclidean_dist_pow_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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__euclidean_dist_pow_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = 0.0
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = libdevice.sqrt(tmp2)
    tmp4 = tmp3 * tmp3
    tl.store(out_ptr0 + (x0), tmp4, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 6), (6, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cdist], Original ATen: [aten._euclidean_dist]
        stream0 = get_raw_stream(0)
        triton_poi_fused__euclidean_dist_0.run(primals_1, buf0, 384, grid=grid(384), stream=stream0)
        buf1 = empty_strided_cuda((4, 6), (6, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cdist], Original ATen: [aten._euclidean_dist]
        triton_poi_fused__euclidean_dist_1.run(primals_2, buf1, 24, grid=grid(24), stream=stream0)
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cdist], Original ATen: [aten._euclidean_dist]
        extern_kernels.mm(buf0, reinterpret_tensor(buf1, (6, 4), (1, 6), 0), out=buf2)
        del buf0
        del buf1
        buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cdist, dis_2], Original ATen: [aten._euclidean_dist, aten.pow]
        triton_poi_fused__euclidean_dist_pow_2.run(buf2, buf3, 256, grid=grid(256), stream=stream0)
    return (buf3, primals_2, reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), buf2, )


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, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2])
    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
import torch.optim
import torch.utils.data


class Prototype(torch.nn.Module):
    """"""

    def __init__(self, prototype_num, latent_size) ->None:
        super(Prototype, self).__init__()
        self.latent_size = latent_size
        self.prototype_num = prototype_num
        self.prototypes = torch.nn.Parameter(data=torch.rand(self.
            prototype_num, self.latent_size), requires_grad=True)

    def forward(self, input):
        """"""
        x = input.view(-1, self.latent_size)
        dis_2 = self.__calc_dist_2(x, self.prototypes)
        return dis_2, self.prototypes

    def __calc_dist_2(self, x, y):
        """"""
        dis_2 = torch.cdist(x, y, p=2) ** 2
        return dis_2


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


def get_init_inputs():
    return [[], {'prototype_num': 4, 'latent_size': 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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn
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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused__euclidean_dist_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 6
    x1 = xindex // 6
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = -2.0
    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], 5, tl.int64)
    tmp12 = tmp0 < tmp11
    tmp13 = tmp10 & tmp12
    tmp14 = tl.load(in_ptr0 + 4 * x1, tmp13 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp15 = tmp14 * tmp14
    tmp16 = tl.load(in_ptr0 + (1 + 4 * x1), tmp13 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp17 = tmp16 * tmp16
    tmp18 = tmp15 + tmp17
    tmp19 = tl.load(in_ptr0 + (2 + 4 * x1), tmp13 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp22 = tl.load(in_ptr0 + (3 + 4 * x1), tmp13 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp23 = tmp22 * tmp22
    tmp24 = tmp21 + tmp23
    tmp25 = tl.full(tmp24.shape, 0.0, tmp24.dtype)
    tmp26 = tl.where(tmp13, tmp24, tmp25)
    tmp27 = tmp0 >= tmp11
    tl.full([1], 6, tl.int64)
    tmp30 = 1.0
    tmp31 = tl.full(tmp30.shape, 0.0, tmp30.dtype)
    tmp32 = tl.where(tmp27, tmp30, tmp31)
    tmp33 = tl.where(tmp13, tmp26, tmp32)
    tmp34 = tl.where(tmp4, tmp9, tmp33)
    tl.store(out_ptr0 + x2, tmp34, xmask)


@triton.jit
def triton_poi_fused__euclidean_dist_1(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 24
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 6
    x1 = xindex // 6
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 5, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = 1.0
    tmp11 = tl.full(tmp10.shape, 0.0, tmp10.dtype)
    tmp12 = tl.where(tmp9, tmp10, tmp11)
    tmp13 = tmp0 >= tmp7
    tl.full([1], 6, tl.int64)
    tmp16 = tl.load(in_ptr0 + 4 * x1, tmp13 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp17 = tmp16 * tmp16
    tmp18 = tl.load(in_ptr0 + (1 + 4 * x1), tmp13 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp19 = tmp18 * tmp18
    tmp20 = tmp17 + tmp19
    tmp21 = tl.load(in_ptr0 + (2 + 4 * x1), tmp13 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp22 = tmp21 * tmp21
    tmp23 = tmp20 + tmp22
    tmp24 = tl.load(in_ptr0 + (3 + 4 * x1), tmp13 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp25 = tmp24 * tmp24
    tmp26 = tmp23 + tmp25
    tmp27 = tl.full(tmp26.shape, 0.0, tmp26.dtype)
    tmp28 = tl.where(tmp13, tmp26, tmp27)
    tmp29 = tl.where(tmp9, tmp12, tmp28)
    tmp30 = tl.where(tmp4, tmp5, tmp29)
    tl.store(out_ptr0 + x2, tmp30, xmask)


@triton.jit
def triton_poi_fused__euclidean_dist_pow_2(in_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.0
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = libdevice.sqrt(tmp2)
    tmp4 = tmp3 * tmp3
    tl.store(out_ptr0 + x0, tmp4, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 6), (6, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__euclidean_dist_0[grid(384)](primals_1, buf0, 384,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf1 = empty_strided_cuda((4, 6), (6, 1), torch.float32)
        triton_poi_fused__euclidean_dist_1[grid(24)](primals_2, buf1, 24,
            XBLOCK=32, num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf0, reinterpret_tensor(buf1, (6, 4), (1, 6), 0),
            out=buf2)
        del buf0
        del buf1
        buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        triton_poi_fused__euclidean_dist_pow_2[grid(256)](buf2, buf3, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
    return buf3, primals_2, reinterpret_tensor(primals_1, (64, 4), (4, 1), 0
        ), buf2


class PrototypeNew(torch.nn.Module):
    """"""

    def __init__(self, prototype_num, latent_size) ->None:
        super(PrototypeNew, self).__init__()
        self.latent_size = latent_size
        self.prototype_num = prototype_num
        self.prototypes = torch.nn.Parameter(data=torch.rand(self.
            prototype_num, self.latent_size), requires_grad=True)

    def __calc_dist_2(self, x, y):
        """"""
        dis_2 = torch.cdist(x, y, p=2) ** 2
        return dis_2

    def forward(self, input_0):
        primals_2 = self.prototypes
        primals_1 = input_0
        output = call([primals_1, primals_2])
        return output[0], output[1]
