# 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/jw/cjwwtxqc62qhaeh6b442qgmivomt7prmutenacaxcfcty66ej5uh.py
# Topologically Sorted Source Nodes: [repeat_1, attn_input], Original ATen: [aten.repeat, aten.add]
# Source node to ATen node mapping:
#   attn_input => add
#   repeat_1 => repeat_1
# Graph fragment:
#   %repeat_1 : [num_users=1] = call_function[target=torch.ops.aten.repeat.default](args = (%view_3, [1, 4, 1]), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_4, %repeat_1), kwargs = {})
triton_poi_fused_add_repeat_0 = async_compile.triton('triton_poi_fused_add_repeat_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: '*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_add_repeat_0', 'mutated_arg_names': [], '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_add_repeat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    x1 = (xindex // 16)
    tmp0 = tl.load(in_ptr0 + ((x2 // 4)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + ((4*x1) + (x0 % 4)), xmask)
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/63/c63xqk3tzlqs2atyt37grfjegiptngv7xjjf3uoqu6zbyydpobqb.py
# Topologically Sorted Source Nodes: [attn_output_1, attention], Original ATen: [aten.leaky_relu, aten._softmax]
# Source node to ATen node mapping:
#   attention => amax, exp, sub
#   attn_output_1 => gt, mul, where
# Graph fragment:
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_5, 0), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_5, 0.2), kwargs = {})
#   %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%gt, %view_5, %mul), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%where, [2], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%where, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
triton_poi_fused__softmax_leaky_relu_1 = async_compile.triton('triton_poi_fused__softmax_leaky_relu_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=[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__softmax_leaky_relu_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__softmax_leaky_relu_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp6 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tmp7 = tmp6 > tmp1
    tmp8 = tmp6 * tmp3
    tmp9 = tl.where(tmp7, tmp6, tmp8)
    tmp11 = tmp10 > tmp1
    tmp12 = tmp10 * tmp3
    tmp13 = tl.where(tmp11, tmp10, tmp12)
    tmp14 = triton_helpers.maximum(tmp9, tmp13)
    tmp16 = tmp15 > tmp1
    tmp17 = tmp15 * tmp3
    tmp18 = tl.where(tmp16, tmp15, tmp17)
    tmp19 = triton_helpers.maximum(tmp14, tmp18)
    tmp21 = tmp20 > tmp1
    tmp22 = tmp20 * tmp3
    tmp23 = tl.where(tmp21, tmp20, tmp22)
    tmp24 = triton_helpers.maximum(tmp19, tmp23)
    tmp25 = tmp5 - tmp24
    tmp26 = tl_math.exp(tmp25)
    tl.store(out_ptr0 + (x2), tmp26, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/em/cem6qbxwbiqnjqybzk5arf2obt5uggy4qs7otwwpovvnrhvdc6h4.py
# Topologically Sorted Source Nodes: [attention], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   attention => div, sum_1
# Graph fragment:
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [2], True), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_2 = async_compile.triton('triton_poi_fused__softmax_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=[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__softmax_2', '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__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + (x2), tmp8, 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, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 1), (1, 1))
    assert_size_stride(primals_3, (4, 1), (1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [key], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), primals_2, out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [query], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), primals_3, out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((4, 16, 1), (16, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [repeat_1, attn_input], Original ATen: [aten.repeat, aten.add]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_repeat_0.run(buf0, buf1, buf2, 64, grid=grid(64), stream=stream0)
        del buf0
        del buf1
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attn_output_1, attention], Original ATen: [aten.leaky_relu, aten._softmax]
        triton_poi_fused__softmax_leaky_relu_1.run(buf2, buf3, 64, grid=grid(64), stream=stream0)
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attention], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_2.run(buf3, buf4, 64, grid=grid(64), stream=stream0)
        buf5 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [attention, matmul_2], Original ATen: [aten._softmax, aten.bmm]
        extern_kernels.bmm(buf4, primals_1, out=buf5)
        del buf4
    return (reinterpret_tensor(buf5, (4, 4, 4), (16, 1, 4), 0), primals_1, reinterpret_tensor(buf2, (4, 4, 4), (16, 4, 1), 0), )


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), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 1), (1, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 1), (1, 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.nn.functional as F


class GAT(nn.Module):

    def __init__(self, num_feats):
        super(GAT, self).__init__()
        self.num_feats = num_feats
        self.weight_key = nn.Parameter(torch.zeros(size=(self.num_feats, 1)))
        self.weight_query = nn.Parameter(torch.zeros(size=(self.num_feats, 1)))
        nn.init.xavier_uniform_(self.weight_key, gain=1.414)
        nn.init.xavier_uniform_(self.weight_query, gain=1.414)
        self.dropout = nn.Dropout(0.5)

    def forward(self, x):
        """
        :param x: dim: bz x num_node x num_feat
        :return: dim: bz x num_node x num_node
        """
        batch_size = x.size(0)
        num_nodes = x.size(1)
        key = torch.matmul(x, self.weight_key)
        query = torch.matmul(x, self.weight_query)
        attn_input = key.repeat(1, 1, num_nodes).view(batch_size, num_nodes *
            num_nodes, 1) + query.repeat(1, num_nodes, 1)
        attn_output = attn_input.squeeze(2).view(batch_size, num_nodes,
            num_nodes)
        attn_output = F.leaky_relu(attn_output, negative_slope=0.2)
        attention = F.softmax(attn_output, dim=2)
        attention = self.dropout(attention)
        attn_feat = torch.matmul(attention, x).permute(0, 2, 1)
        return attn_feat


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


def get_init_inputs():
    return [[], {'num_feats': 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 math as tl_math
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_add_repeat_0(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    x1 = xindex // 16
    tmp0 = tl.load(in_ptr0 + x2 // 4, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (4 * x1 + x0 % 4), xmask)
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + x2, tmp2, xmask)


@triton.jit
def triton_poi_fused__softmax_leaky_relu_1(in_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp6 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp15 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp20 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tmp7 = tmp6 > tmp1
    tmp8 = tmp6 * tmp3
    tmp9 = tl.where(tmp7, tmp6, tmp8)
    tmp11 = tmp10 > tmp1
    tmp12 = tmp10 * tmp3
    tmp13 = tl.where(tmp11, tmp10, tmp12)
    tmp14 = triton_helpers.maximum(tmp9, tmp13)
    tmp16 = tmp15 > tmp1
    tmp17 = tmp15 * tmp3
    tmp18 = tl.where(tmp16, tmp15, tmp17)
    tmp19 = triton_helpers.maximum(tmp14, tmp18)
    tmp21 = tmp20 > tmp1
    tmp22 = tmp20 * tmp3
    tmp23 = tl.where(tmp21, tmp20, tmp22)
    tmp24 = triton_helpers.maximum(tmp19, tmp23)
    tmp25 = tmp5 - tmp24
    tmp26 = tl_math.exp(tmp25)
    tl.store(out_ptr0 + x2, tmp26, xmask)


@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 1), (1, 1))
    assert_size_stride(primals_3, (4, 1), (1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            primals_2, out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            primals_3, out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((4, 16, 1), (16, 1, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_repeat_0[grid(64)](buf0, buf1, buf2, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del buf0
        del buf1
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_leaky_relu_1[grid(64)](buf2, buf3, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_2[grid(64)](buf3, buf4, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf5 = buf3
        del buf3
        extern_kernels.bmm(buf4, primals_1, out=buf5)
        del buf4
    return reinterpret_tensor(buf5, (4, 4, 4), (16, 1, 4), 0
        ), primals_1, reinterpret_tensor(buf2, (4, 4, 4), (16, 4, 1), 0)


class GATNew(nn.Module):

    def __init__(self, num_feats):
        super(GATNew, self).__init__()
        self.num_feats = num_feats
        self.weight_key = nn.Parameter(torch.zeros(size=(self.num_feats, 1)))
        self.weight_query = nn.Parameter(torch.zeros(size=(self.num_feats, 1)))
        nn.init.xavier_uniform_(self.weight_key, gain=1.414)
        nn.init.xavier_uniform_(self.weight_query, gain=1.414)
        self.dropout = nn.Dropout(0.5)

    def forward(self, input_0):
        primals_2 = self.weight_key
        primals_3 = self.weight_query
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
