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


class GraphAttentionLayer(nn.Module):
    """
    Simple GAT layer, similar to https://arxiv.org/abs/1710.10903
    """

    def __init__(self, in_features, out_features, dropout, alpha, concat=True):
        super(GraphAttentionLayer, self).__init__()
        self.dropout = dropout
        self.in_features = in_features
        self.out_features = out_features
        self.alpha = alpha
        self.concat = concat
        self.W = nn.Parameter(torch.empty(size=(in_features, out_features)))
        nn.init.xavier_uniform_(self.W.data, gain=1.414)
        self.a = nn.Parameter(torch.empty(size=(2 * out_features, 1)))
        nn.init.xavier_uniform_(self.a.data, gain=1.414)
        self.leakyrelu = nn.LeakyReLU(self.alpha)

    def forward(self, h, adj):
        Wh = torch.mm(h, self.W)
        a_input = self._prepare_attentional_mechanism_input(Wh)
        e = self.leakyrelu(torch.matmul(a_input, self.a).squeeze(2))
        zero_vec = -9000000000000000.0 * torch.ones_like(e)
        attention = torch.where(adj > 0, e, zero_vec)
        attention = F.softmax(attention, dim=1)
        attention = F.dropout(attention, self.dropout, training=self.training)
        h_prime = torch.matmul(attention, Wh)
        if self.concat:
            return F.elu(h_prime)
        else:
            return h_prime

    def _prepare_attentional_mechanism_input(self, Wh):
        N = Wh.size()[0]
        Wh_repeated_in_chunks = Wh.repeat_interleave(N, dim=0)
        Wh_repeated_alternating = Wh.repeat(N, 1)
        all_combinations_matrix = torch.cat([Wh_repeated_in_chunks,
            Wh_repeated_alternating], dim=1)
        return all_combinations_matrix.view(N, N, 2 * self.out_features)

    def __repr__(self):
        return self.__class__.__name__ + ' (' + str(self.in_features
            ) + ' -> ' + str(self.out_features) + ')'


class GAT(nn.Module):

    def __init__(self, nfeat, nhid, nclass, dropout, alpha, nheads):
        """Dense version of GAT."""
        super(GAT, self).__init__()
        self.dropout = dropout
        self.attentions = [GraphAttentionLayer(nfeat, nhid, dropout=dropout,
            alpha=alpha, concat=True) for _ in range(nheads)]
        for i, attention in enumerate(self.attentions):
            self.add_module('attention_{}'.format(i), attention)
        self.out_att = GraphAttentionLayer(nhid * nheads, nclass, dropout=
            dropout, alpha=alpha, concat=False)

    def forward(self, x, adj):
        x = F.dropout(x, self.dropout, training=self.training)
        x = torch.cat([att(x, adj) for att in self.attentions], dim=1)
        x = F.dropout(x, self.dropout, training=self.training)
        x = F.elu(self.out_att(x, adj))
        return F.log_softmax(x, dim=1)


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


def get_init_inputs():
    return [[], {'nfeat': 4, 'nhid': 4, 'nclass': 4, 'dropout': 0.5,
        'alpha': 4, 'nheads': 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, math as tl_math
import torch.nn as nn
import torch.nn.functional as F
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_cat_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = xindex // 8
    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 // 4) + x0), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr0 + (4 * (x1 % 4) + (-4 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_leaky_relu_1(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
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused__softmax_leaky_relu_mul_where_2(in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, in_ptr8, out_ptr0,
    out_ptr1, out_ptr2, out_ptr3, out_ptr4, out_ptr5, out_ptr6, out_ptr7,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last').to(tl
        .int1)
    tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last').to(tl
        .int1)
    tmp2 = tl.load(in_ptr2 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp9 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp10 = tl.load(in_ptr2 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp15 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp16 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp17 = tl.load(in_ptr2 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp22 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp23 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp24 = tl.load(in_ptr2 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp40 = tl.load(in_ptr3 + 4 * x0, xmask, eviction_policy='evict_last').to(
        tl.int1)
    tmp41 = tl.load(in_ptr4 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp45 = tl.load(in_ptr3 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp46 = tl.load(in_ptr4 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp51 = tl.load(in_ptr3 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp52 = tl.load(in_ptr4 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp57 = tl.load(in_ptr3 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp58 = tl.load(in_ptr4 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp74 = tl.load(in_ptr5 + 4 * x0, xmask, eviction_policy='evict_last').to(
        tl.int1)
    tmp75 = tl.load(in_ptr6 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp79 = tl.load(in_ptr5 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp80 = tl.load(in_ptr6 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp85 = tl.load(in_ptr5 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp86 = tl.load(in_ptr6 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp91 = tl.load(in_ptr5 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp92 = tl.load(in_ptr6 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp108 = tl.load(in_ptr7 + 4 * x0, xmask, eviction_policy='evict_last').to(
        tl.int1)
    tmp109 = tl.load(in_ptr8 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp113 = tl.load(in_ptr7 + (1 + 4 * x0), xmask, eviction_policy=
        'evict_last').to(tl.int1)
    tmp114 = tl.load(in_ptr8 + (1 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp119 = tl.load(in_ptr7 + (2 + 4 * x0), xmask, eviction_policy=
        'evict_last').to(tl.int1)
    tmp120 = tl.load(in_ptr8 + (2 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp125 = tl.load(in_ptr7 + (3 + 4 * x0), xmask, eviction_policy=
        'evict_last').to(tl.int1)
    tmp126 = tl.load(in_ptr8 + (3 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp3 = 4.0
    tmp4 = tmp2 * tmp3
    tmp5 = tl.where(tmp1, tmp2, tmp4)
    tmp6 = -8999999815811072.0
    tmp7 = tl.where(tmp0, tmp5, tmp6)
    tmp11 = tmp10 * tmp3
    tmp12 = tl.where(tmp9, tmp10, tmp11)
    tmp13 = tl.where(tmp8, tmp12, tmp6)
    tmp14 = triton_helpers.maximum(tmp7, tmp13)
    tmp18 = tmp17 * tmp3
    tmp19 = tl.where(tmp16, tmp17, tmp18)
    tmp20 = tl.where(tmp15, tmp19, tmp6)
    tmp21 = triton_helpers.maximum(tmp14, tmp20)
    tmp25 = tmp24 * tmp3
    tmp26 = tl.where(tmp23, tmp24, tmp25)
    tmp27 = tl.where(tmp22, tmp26, tmp6)
    tmp28 = triton_helpers.maximum(tmp21, tmp27)
    tmp29 = tmp7 - tmp28
    tmp30 = tl_math.exp(tmp29)
    tmp31 = tmp13 - tmp28
    tmp32 = tl_math.exp(tmp31)
    tmp33 = tmp30 + tmp32
    tmp34 = tmp20 - tmp28
    tmp35 = tl_math.exp(tmp34)
    tmp36 = tmp33 + tmp35
    tmp37 = tmp27 - tmp28
    tmp38 = tl_math.exp(tmp37)
    tmp39 = tmp36 + tmp38
    tmp42 = tmp41 * tmp3
    tmp43 = tl.where(tmp40, tmp41, tmp42)
    tmp44 = tl.where(tmp0, tmp43, tmp6)
    tmp47 = tmp46 * tmp3
    tmp48 = tl.where(tmp45, tmp46, tmp47)
    tmp49 = tl.where(tmp8, tmp48, tmp6)
    tmp50 = triton_helpers.maximum(tmp44, tmp49)
    tmp53 = tmp52 * tmp3
    tmp54 = tl.where(tmp51, tmp52, tmp53)
    tmp55 = tl.where(tmp15, tmp54, tmp6)
    tmp56 = triton_helpers.maximum(tmp50, tmp55)
    tmp59 = tmp58 * tmp3
    tmp60 = tl.where(tmp57, tmp58, tmp59)
    tmp61 = tl.where(tmp22, tmp60, tmp6)
    tmp62 = triton_helpers.maximum(tmp56, tmp61)
    tmp63 = tmp44 - tmp62
    tmp64 = tl_math.exp(tmp63)
    tmp65 = tmp49 - tmp62
    tmp66 = tl_math.exp(tmp65)
    tmp67 = tmp64 + tmp66
    tmp68 = tmp55 - tmp62
    tmp69 = tl_math.exp(tmp68)
    tmp70 = tmp67 + tmp69
    tmp71 = tmp61 - tmp62
    tmp72 = tl_math.exp(tmp71)
    tmp73 = tmp70 + tmp72
    tmp76 = tmp75 * tmp3
    tmp77 = tl.where(tmp74, tmp75, tmp76)
    tmp78 = tl.where(tmp0, tmp77, tmp6)
    tmp81 = tmp80 * tmp3
    tmp82 = tl.where(tmp79, tmp80, tmp81)
    tmp83 = tl.where(tmp8, tmp82, tmp6)
    tmp84 = triton_helpers.maximum(tmp78, tmp83)
    tmp87 = tmp86 * tmp3
    tmp88 = tl.where(tmp85, tmp86, tmp87)
    tmp89 = tl.where(tmp15, tmp88, tmp6)
    tmp90 = triton_helpers.maximum(tmp84, tmp89)
    tmp93 = tmp92 * tmp3
    tmp94 = tl.where(tmp91, tmp92, tmp93)
    tmp95 = tl.where(tmp22, tmp94, tmp6)
    tmp96 = triton_helpers.maximum(tmp90, tmp95)
    tmp97 = tmp78 - tmp96
    tmp98 = tl_math.exp(tmp97)
    tmp99 = tmp83 - tmp96
    tmp100 = tl_math.exp(tmp99)
    tmp101 = tmp98 + tmp100
    tmp102 = tmp89 - tmp96
    tmp103 = tl_math.exp(tmp102)
    tmp104 = tmp101 + tmp103
    tmp105 = tmp95 - tmp96
    tmp106 = tl_math.exp(tmp105)
    tmp107 = tmp104 + tmp106
    tmp110 = tmp109 * tmp3
    tmp111 = tl.where(tmp108, tmp109, tmp110)
    tmp112 = tl.where(tmp0, tmp111, tmp6)
    tmp115 = tmp114 * tmp3
    tmp116 = tl.where(tmp113, tmp114, tmp115)
    tmp117 = tl.where(tmp8, tmp116, tmp6)
    tmp118 = triton_helpers.maximum(tmp112, tmp117)
    tmp121 = tmp120 * tmp3
    tmp122 = tl.where(tmp119, tmp120, tmp121)
    tmp123 = tl.where(tmp15, tmp122, tmp6)
    tmp124 = triton_helpers.maximum(tmp118, tmp123)
    tmp127 = tmp126 * tmp3
    tmp128 = tl.where(tmp125, tmp126, tmp127)
    tmp129 = tl.where(tmp22, tmp128, tmp6)
    tmp130 = triton_helpers.maximum(tmp124, tmp129)
    tmp131 = tmp112 - tmp130
    tmp132 = tl_math.exp(tmp131)
    tmp133 = tmp117 - tmp130
    tmp134 = tl_math.exp(tmp133)
    tmp135 = tmp132 + tmp134
    tmp136 = tmp123 - tmp130
    tmp137 = tl_math.exp(tmp136)
    tmp138 = tmp135 + tmp137
    tmp139 = tmp129 - tmp130
    tmp140 = tl_math.exp(tmp139)
    tmp141 = tmp138 + tmp140
    tl.store(out_ptr0 + x0, tmp28, xmask)
    tl.store(out_ptr1 + x0, tmp39, xmask)
    tl.store(out_ptr2 + x0, tmp62, xmask)
    tl.store(out_ptr3 + x0, tmp73, xmask)
    tl.store(out_ptr4 + x0, tmp96, xmask)
    tl.store(out_ptr5 + x0, tmp107, xmask)
    tl.store(out_ptr6 + x0, tmp130, xmask)
    tl.store(out_ptr7 + x0, tmp141, xmask)


@triton.jit
def triton_poi_fused__softmax_leaky_relu_mul_where_3(in_out_ptr0,
    in_out_ptr1, in_out_ptr2, in_out_ptr3, in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, in_ptr8, in_ptr9, in_ptr10,
    in_ptr11, in_ptr12, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    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).to(tl.int1)
    tmp1 = tl.load(in_ptr1 + x2, xmask).to(tl.int1)
    tmp2 = tl.load(in_out_ptr0 + x2, xmask)
    tmp8 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr4 + x2, xmask).to(tl.int1)
    tmp14 = tl.load(in_out_ptr1 + x2, xmask)
    tmp18 = tl.load(in_ptr5 + x1, xmask, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr6 + x1, xmask, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr7 + x2, xmask).to(tl.int1)
    tmp24 = tl.load(in_out_ptr2 + x2, xmask)
    tmp28 = tl.load(in_ptr8 + x1, xmask, eviction_policy='evict_last')
    tmp31 = tl.load(in_ptr9 + x1, xmask, eviction_policy='evict_last')
    tmp33 = tl.load(in_ptr10 + x2, xmask).to(tl.int1)
    tmp34 = tl.load(in_out_ptr3 + x2, xmask)
    tmp38 = tl.load(in_ptr11 + x1, xmask, eviction_policy='evict_last')
    tmp41 = tl.load(in_ptr12 + x1, xmask, eviction_policy='evict_last')
    tmp3 = 4.0
    tmp4 = tmp2 * tmp3
    tmp5 = tl.where(tmp1, tmp2, tmp4)
    tmp6 = -8999999815811072.0
    tmp7 = tl.where(tmp0, tmp5, tmp6)
    tmp9 = tmp7 - tmp8
    tmp10 = tl_math.exp(tmp9)
    tmp12 = tmp10 / tmp11
    tmp15 = tmp14 * tmp3
    tmp16 = tl.where(tmp13, tmp14, tmp15)
    tmp17 = tl.where(tmp0, tmp16, tmp6)
    tmp19 = tmp17 - tmp18
    tmp20 = tl_math.exp(tmp19)
    tmp22 = tmp20 / tmp21
    tmp25 = tmp24 * tmp3
    tmp26 = tl.where(tmp23, tmp24, tmp25)
    tmp27 = tl.where(tmp0, tmp26, tmp6)
    tmp29 = tmp27 - tmp28
    tmp30 = tl_math.exp(tmp29)
    tmp32 = tmp30 / tmp31
    tmp35 = tmp34 * tmp3
    tmp36 = tl.where(tmp33, tmp34, tmp35)
    tmp37 = tl.where(tmp0, tmp36, tmp6)
    tmp39 = tmp37 - tmp38
    tmp40 = tl_math.exp(tmp39)
    tmp42 = tmp40 / tmp41
    tl.store(in_out_ptr0 + x2, tmp12, xmask)
    tl.store(in_out_ptr1 + x2, tmp22, xmask)
    tl.store(in_out_ptr2 + x2, tmp32, xmask)
    tl.store(in_out_ptr3 + x2, tmp42, xmask)


@triton.jit
def triton_poi_fused_cat_4(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = xindex // 16
    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 = 0.0
    tmp7 = tmp5 > tmp6
    tmp8 = 1.0
    tmp9 = tmp5 * tmp8
    tmp10 = libdevice.expm1(tmp9)
    tmp11 = tmp10 * tmp8
    tmp12 = tl.where(tmp7, tmp9, tmp11)
    tmp13 = tl.full(tmp12.shape, 0.0, tmp12.dtype)
    tmp14 = tl.where(tmp4, tmp12, tmp13)
    tmp15 = tmp0 >= tmp3
    tmp16 = tl.full([1], 8, tl.int64)
    tmp17 = tmp0 < tmp16
    tmp18 = tmp15 & tmp17
    tmp19 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp18 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp20 = tmp19 > tmp6
    tmp21 = tmp19 * tmp8
    tmp22 = libdevice.expm1(tmp21)
    tmp23 = tmp22 * tmp8
    tmp24 = tl.where(tmp20, tmp21, tmp23)
    tmp25 = tl.full(tmp24.shape, 0.0, tmp24.dtype)
    tmp26 = tl.where(tmp18, tmp24, tmp25)
    tmp27 = tmp0 >= tmp16
    tmp28 = tl.full([1], 12, tl.int64)
    tmp29 = tmp0 < tmp28
    tmp30 = tmp27 & tmp29
    tmp31 = tl.load(in_ptr2 + (4 * x1 + (-8 + x0)), tmp30 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp32 = tmp31 > tmp6
    tmp33 = tmp31 * tmp8
    tmp34 = libdevice.expm1(tmp33)
    tmp35 = tmp34 * tmp8
    tmp36 = tl.where(tmp32, tmp33, tmp35)
    tmp37 = tl.full(tmp36.shape, 0.0, tmp36.dtype)
    tmp38 = tl.where(tmp30, tmp36, tmp37)
    tmp39 = tmp0 >= tmp28
    tl.full([1], 16, tl.int64)
    tmp42 = tl.load(in_ptr3 + (4 * x1 + (-12 + x0)), tmp39 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp43 = tmp42 > tmp6
    tmp44 = tmp42 * tmp8
    tmp45 = libdevice.expm1(tmp44)
    tmp46 = tmp45 * tmp8
    tmp47 = tl.where(tmp43, tmp44, tmp46)
    tmp48 = tl.full(tmp47.shape, 0.0, tmp47.dtype)
    tmp49 = tl.where(tmp39, tmp47, tmp48)
    tmp50 = tl.where(tmp30, tmp38, tmp49)
    tmp51 = tl.where(tmp18, tmp26, tmp50)
    tmp52 = tl.where(tmp4, tmp14, tmp51)
    tl.store(out_ptr0 + x2, tmp52, xmask)


@triton.jit
def triton_poi_fused__softmax_leaky_relu_mul_where_5(in_ptr0, in_ptr1,
    in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last').to(tl
        .int1)
    tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last').to(tl
        .int1)
    tmp2 = tl.load(in_ptr2 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp9 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp10 = tl.load(in_ptr2 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp15 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp16 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp17 = tl.load(in_ptr2 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp22 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp23 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp24 = tl.load(in_ptr2 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp3 = 4.0
    tmp4 = tmp2 * tmp3
    tmp5 = tl.where(tmp1, tmp2, tmp4)
    tmp6 = -8999999815811072.0
    tmp7 = tl.where(tmp0, tmp5, tmp6)
    tmp11 = tmp10 * tmp3
    tmp12 = tl.where(tmp9, tmp10, tmp11)
    tmp13 = tl.where(tmp8, tmp12, tmp6)
    tmp14 = triton_helpers.maximum(tmp7, tmp13)
    tmp18 = tmp17 * tmp3
    tmp19 = tl.where(tmp16, tmp17, tmp18)
    tmp20 = tl.where(tmp15, tmp19, tmp6)
    tmp21 = triton_helpers.maximum(tmp14, tmp20)
    tmp25 = tmp24 * tmp3
    tmp26 = tl.where(tmp23, tmp24, tmp25)
    tmp27 = tl.where(tmp22, tmp26, tmp6)
    tmp28 = triton_helpers.maximum(tmp21, tmp27)
    tmp29 = tmp7 - tmp28
    tmp30 = tl_math.exp(tmp29)
    tmp31 = tmp13 - tmp28
    tmp32 = tl_math.exp(tmp31)
    tmp33 = tmp30 + tmp32
    tmp34 = tmp20 - tmp28
    tmp35 = tl_math.exp(tmp34)
    tmp36 = tmp33 + tmp35
    tmp37 = tmp27 - tmp28
    tmp38 = tl_math.exp(tmp37)
    tmp39 = tmp36 + tmp38
    tl.store(out_ptr0 + x0, tmp28, xmask)
    tl.store(out_ptr1 + x0, tmp39, xmask)


@triton.jit
def triton_poi_fused__softmax_leaky_relu_mul_where_6(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    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).to(tl.int1)
    tmp1 = tl.load(in_ptr1 + x2, xmask).to(tl.int1)
    tmp2 = tl.load(in_out_ptr0 + x2, xmask)
    tmp8 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp3 = 4.0
    tmp4 = tmp2 * tmp3
    tmp5 = tl.where(tmp1, tmp2, tmp4)
    tmp6 = -8999999815811072.0
    tmp7 = tl.where(tmp0, tmp5, tmp6)
    tmp9 = tmp7 - tmp8
    tmp10 = tl_math.exp(tmp9)
    tmp12 = tmp10 / tmp11
    tl.store(in_out_ptr0 + x2, tmp12, xmask)


@triton.jit
def triton_poi_fused__log_softmax_elu_7(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
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp8 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp21 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp28 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 1.0
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.expm1(tmp4)
    tmp6 = tmp5 * tmp3
    tmp7 = tl.where(tmp2, tmp4, tmp6)
    tmp9 = tmp8 > tmp1
    tmp10 = tmp8 * tmp3
    tmp11 = libdevice.expm1(tmp10)
    tmp12 = tmp11 * tmp3
    tmp13 = tl.where(tmp9, tmp10, tmp12)
    tmp15 = tmp14 > tmp1
    tmp16 = tmp14 * tmp3
    tmp17 = libdevice.expm1(tmp16)
    tmp18 = tmp17 * tmp3
    tmp19 = tl.where(tmp15, tmp16, tmp18)
    tmp20 = triton_helpers.maximum(tmp13, tmp19)
    tmp22 = tmp21 > tmp1
    tmp23 = tmp21 * tmp3
    tmp24 = libdevice.expm1(tmp23)
    tmp25 = tmp24 * tmp3
    tmp26 = tl.where(tmp22, tmp23, tmp25)
    tmp27 = triton_helpers.maximum(tmp20, tmp26)
    tmp29 = tmp28 > tmp1
    tmp30 = tmp28 * tmp3
    tmp31 = libdevice.expm1(tmp30)
    tmp32 = tmp31 * tmp3
    tmp33 = tl.where(tmp29, tmp30, tmp32)
    tmp34 = triton_helpers.maximum(tmp27, tmp33)
    tmp35 = tmp7 - tmp34
    tl.store(out_ptr0 + x2, tmp35, xmask)


@triton.jit
def triton_poi_fused__log_softmax_8(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
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp2 = tl_math.exp(tmp1)
    tmp4 = tl_math.exp(tmp3)
    tmp5 = tmp2 + tmp4
    tmp7 = tl_math.exp(tmp6)
    tmp8 = tmp5 + tmp7
    tmp10 = tl_math.exp(tmp9)
    tmp11 = tmp8 + tmp10
    tmp12 = tl_math.log(tmp11)
    tmp13 = tmp0 - tmp12
    tl.store(out_ptr0 + x2, tmp13, 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
        ) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (8, 1), (1, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (8, 1), (1, 1))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (8, 1), (1, 1))
    assert_size_stride(primals_9, (4, 4), (4, 1))
    assert_size_stride(primals_10, (8, 1), (1, 1))
    assert_size_stride(primals_11, (16, 4), (4, 1))
    assert_size_stride(primals_12, (8, 1), (1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_1, primals_2, out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((16, 8), (8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(128)](buf0, buf1, 128, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf2 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        extern_kernels.mm(buf1, primals_3, out=buf2)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        triton_poi_fused_leaky_relu_1[grid(16)](buf2, buf3, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        triton_poi_fused_leaky_relu_1[grid(16)](primals_4, buf4, 16, XBLOCK
            =16, num_warps=1, num_stages=1)
        del primals_4
        buf9 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_1, primals_5, out=buf9)
        del primals_5
        buf10 = empty_strided_cuda((16, 8), (8, 1), torch.float32)
        triton_poi_fused_cat_0[grid(128)](buf9, buf10, 128, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf11 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        extern_kernels.mm(buf10, primals_6, out=buf11)
        buf12 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        triton_poi_fused_leaky_relu_1[grid(16)](buf11, buf12, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf17 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_1, primals_7, out=buf17)
        del primals_7
        buf18 = empty_strided_cuda((16, 8), (8, 1), torch.float32)
        triton_poi_fused_cat_0[grid(128)](buf17, buf18, 128, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf19 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        extern_kernels.mm(buf18, primals_8, out=buf19)
        buf20 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        triton_poi_fused_leaky_relu_1[grid(16)](buf19, buf20, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf25 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_1, primals_9, out=buf25)
        del primals_9
        buf26 = empty_strided_cuda((16, 8), (8, 1), torch.float32)
        triton_poi_fused_cat_0[grid(128)](buf25, buf26, 128, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf27 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        extern_kernels.mm(buf26, primals_10, out=buf27)
        buf28 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        triton_poi_fused_leaky_relu_1[grid(16)](buf27, buf28, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf5 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        buf6 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        buf13 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        buf14 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        buf21 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        buf22 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        buf29 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        buf30 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        triton_poi_fused__softmax_leaky_relu_mul_where_2[grid(4)](buf4,
            buf3, buf2, buf12, buf11, buf20, buf19, buf28, buf27, buf5,
            buf6, buf13, buf14, buf21, buf22, buf29, buf30, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        buf7 = reinterpret_tensor(buf2, (4, 4), (4, 1), 0)
        del buf2
        buf15 = reinterpret_tensor(buf11, (4, 4), (4, 1), 0)
        del buf11
        buf23 = reinterpret_tensor(buf19, (4, 4), (4, 1), 0)
        del buf19
        buf31 = reinterpret_tensor(buf27, (4, 4), (4, 1), 0)
        del buf27
        triton_poi_fused__softmax_leaky_relu_mul_where_3[grid(16)](buf7,
            buf15, buf23, buf31, buf4, buf3, buf5, buf6, buf12, buf13,
            buf14, buf20, buf21, buf22, buf28, buf29, buf30, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del buf13
        del buf14
        del buf21
        del buf22
        del buf29
        del buf30
        buf8 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf7, buf0, out=buf8)
        buf16 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf15, buf9, out=buf16)
        buf24 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf23, buf17, out=buf24)
        buf32 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf31, buf25, out=buf32)
        buf33 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        triton_poi_fused_cat_4[grid(64)](buf8, buf16, buf24, buf32, buf33, 
            64, XBLOCK=64, num_warps=1, num_stages=1)
        buf34 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf33, primals_11, out=buf34)
        buf35 = empty_strided_cuda((16, 8), (8, 1), torch.float32)
        triton_poi_fused_cat_0[grid(128)](buf34, buf35, 128, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf36 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        extern_kernels.mm(buf35, primals_12, out=buf36)
        buf37 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        triton_poi_fused_leaky_relu_1[grid(16)](buf36, buf37, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf38 = buf6
        del buf6
        buf39 = buf5
        del buf5
        triton_poi_fused__softmax_leaky_relu_mul_where_5[grid(4)](buf4,
            buf37, buf36, buf38, buf39, 4, XBLOCK=4, num_warps=1, num_stages=1)
        buf40 = reinterpret_tensor(buf36, (4, 4), (4, 1), 0)
        del buf36
        triton_poi_fused__softmax_leaky_relu_mul_where_6[grid(16)](buf40,
            buf4, buf37, buf38, buf39, 16, XBLOCK=16, num_warps=1, num_stages=1
            )
        del buf38
        del buf39
        buf41 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf40, buf34, out=buf41)
        buf42 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused__log_softmax_elu_7[grid(16)](buf41, buf42, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        buf43 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused__log_softmax_8[grid(16)](buf42, buf43, 16, XBLOCK=
            16, num_warps=1, num_stages=1)
        del buf42
    return (buf43, buf3, buf4, buf7, buf8, buf12, buf15, buf16, buf20,
        buf23, buf24, buf28, buf31, buf32, buf37, buf40, buf41, buf43,
        reinterpret_tensor(buf34, (4, 4), (1, 4), 0), reinterpret_tensor(
        buf35, (8, 16), (1, 8), 0), reinterpret_tensor(primals_12, (1, 8),
        (1, 1), 0), reinterpret_tensor(buf33, (16, 4), (1, 16), 0),
        reinterpret_tensor(primals_11, (4, 16), (1, 4), 0),
        reinterpret_tensor(buf25, (4, 4), (1, 4), 0), reinterpret_tensor(
        buf26, (8, 16), (1, 8), 0), reinterpret_tensor(primals_10, (1, 8),
        (1, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0),
        reinterpret_tensor(buf17, (4, 4), (1, 4), 0), reinterpret_tensor(
        buf18, (8, 16), (1, 8), 0), reinterpret_tensor(primals_8, (1, 8), (
        1, 1), 0), reinterpret_tensor(buf9, (4, 4), (1, 4), 0),
        reinterpret_tensor(buf10, (8, 16), (1, 8), 0), reinterpret_tensor(
        primals_6, (1, 8), (1, 1), 0), reinterpret_tensor(buf0, (4, 4), (1,
        4), 0), reinterpret_tensor(buf1, (8, 16), (1, 8), 0),
        reinterpret_tensor(primals_3, (1, 8), (1, 1), 0))


class GraphAttentionLayer(nn.Module):
    """
    Simple GAT layer, similar to https://arxiv.org/abs/1710.10903
    """

    def __init__(self, in_features, out_features, dropout, alpha, concat=True):
        super(GraphAttentionLayer, self).__init__()
        self.dropout = dropout
        self.in_features = in_features
        self.out_features = out_features
        self.alpha = alpha
        self.concat = concat
        self.W = nn.Parameter(torch.empty(size=(in_features, out_features)))
        nn.init.xavier_uniform_(self.W.data, gain=1.414)
        self.a = nn.Parameter(torch.empty(size=(2 * out_features, 1)))
        nn.init.xavier_uniform_(self.a.data, gain=1.414)
        self.leakyrelu = nn.LeakyReLU(self.alpha)

    def forward(self, h, adj):
        Wh = torch.mm(h, self.W)
        a_input = self._prepare_attentional_mechanism_input(Wh)
        e = self.leakyrelu(torch.matmul(a_input, self.a).squeeze(2))
        zero_vec = -9000000000000000.0 * torch.ones_like(e)
        attention = torch.where(adj > 0, e, zero_vec)
        attention = F.softmax(attention, dim=1)
        attention = F.dropout(attention, self.dropout, training=self.training)
        h_prime = torch.matmul(attention, Wh)
        if self.concat:
            return F.elu(h_prime)
        else:
            return h_prime

    def _prepare_attentional_mechanism_input(self, Wh):
        N = Wh.size()[0]
        Wh_repeated_in_chunks = Wh.repeat_interleave(N, dim=0)
        Wh_repeated_alternating = Wh.repeat(N, 1)
        all_combinations_matrix = torch.cat([Wh_repeated_in_chunks,
            Wh_repeated_alternating], dim=1)
        return all_combinations_matrix.view(N, N, 2 * self.out_features)

    def __repr__(self):
        return self.__class__.__name__ + ' (' + str(self.in_features
            ) + ' -> ' + str(self.out_features) + ')'


class GATNew(nn.Module):

    def __init__(self, nfeat, nhid, nclass, dropout, alpha, nheads):
        """Dense version of GAT."""
        super(GATNew, self).__init__()
        self.dropout = dropout
        self.attentions = [GraphAttentionLayer(nfeat, nhid, dropout=dropout,
            alpha=alpha, concat=True) for _ in range(nheads)]
        for i, attention in enumerate(self.attentions):
            self.add_module('attention_{}'.format(i), attention)
        self.out_att = GraphAttentionLayer(nhid * nheads, nclass, dropout=
            dropout, alpha=alpha, concat=False)

    def forward(self, input_0, input_1):
        primals_1 = self.attention_0.W
        primals_3 = self.attention_0.a
        primals_2 = self.attention_1.W
        primals_6 = self.attention_1.a
        primals_4 = self.attention_2.W
        primals_8 = self.attention_2.a
        primals_5 = self.attention_3.W
        primals_10 = self.attention_3.a
        primals_11 = self.out_att.W
        primals_12 = self.out_att.a
        primals_7 = input_0
        primals_9 = input_1
        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])
        return output[0]
