# 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_6/inductor_cache/rl/crljeqnoa6ykpfmvk4fgvc6cahtaak6ilhiaoxyzgcd7ynbpfnj2.py
# Topologically Sorted Source Nodes: [ones], Original ATen: [aten.ones]
# Source node to ATen node mapping:
#   ones => full_default
# Graph fragment:
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([4, 1], 1), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
triton_poi_fused_ones_0 = async_compile.triton('triton_poi_fused_ones_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=[4], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '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,), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_ones_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, '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_ones_0(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = 1.0
    tl.store(out_ptr0 + (x0), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/dn/cdnkxcepcwle4u3a2zcw72axoqvmyd4bn2kvjp72wctqmnh3vrpp.py
# Topologically Sorted Source Nodes: [result, result_1], Original ATen: [aten.div, aten.add]
# Source node to ATen node mapping:
#   result => div
#   result_1 => add
# Graph fragment:
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%view_6, %view_1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%div, %view_8), kwargs = {})
triton_poi_fused_add_div_1 = async_compile.triton('triton_poi_fused_add_div_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '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, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_div_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, '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_div_1(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (x2), xmask)
    tmp4 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 / tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tl.store(in_out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, ), (1, ))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [ones], Original ATen: [aten.ones]
        stream0 = get_raw_stream(0)
        triton_poi_fused_ones_0.run(buf0, 4, grid=grid(4), stream=stream0)
        buf1 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [ones, norm], Original ATen: [aten.ones, aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), buf0, out=buf1)
        del buf0
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_4, reinterpret_tensor(primals_2, (64, 4), (4, 1), 0), reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf2)
        del primals_3
        del primals_4
        buf3 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul_1], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(primals_1, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf2, (16, 4, 4), (16, 4, 1), 0), out=buf3)
        buf4 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf4)
        del primals_5
        buf5 = reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf3  # reuse
        # Topologically Sorted Source Nodes: [result, result_1], Original ATen: [aten.div, aten.add]
        triton_poi_fused_add_div_1.run(buf5, buf1, buf4, primals_6, 256, grid=grid(256), stream=stream0)
        del buf4
        del primals_6
    return (buf5, buf1, reinterpret_tensor(primals_2, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (16, 4, 4), (16, 1, 4), 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, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6])
    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
from torch import nn
import torch.nn
import torch.autograd


def sparse_bmm(sparse_matrix, dense_matrix_batch):
    """
    Perform torch.bmm on an unbatched sparse matrix and a batched dense matrix.

    Args:
        sparse_matrix (torch.sparse.FloatTensor): Shape = (m, n)
        dense_matrix_batch (torch.FloatTensor): Shape = (b, n, p)

    Returns:
        (torch.FloatTensor):
            Result of the batched matrix multiplication. Shape = (b, n, p)
    """
    m = sparse_matrix.shape[0]
    b, n, p = dense_matrix_batch.shape
    dense_matrix = dense_matrix_batch.transpose(0, 1).reshape(n, b * p)
    result = torch.sparse.mm(sparse_matrix, dense_matrix)
    return result.reshape(m, b, p).transpose(0, 1)


class GraphConv(nn.Module):
    """
    A simple graph convolution layer, similar to the one defined in Kipf et al.
    https://arxiv.org/abs/1609.02907

    This operation with self_layer=False is equivalent to :math:`(A H W)` where:
    - :math:`H` is the node features with shape (batch_size, num_nodes, input_dim)
    - :math:`W` is a weight matrix of shape (input_dim, output_dim)
    - :math:`A` is the adjacency matrix of shape (num_nodes, num_nodes).
    It can include self-loop.

    With normalize_adj=True, it is equivalent to :math:`(D^{-1} A H W)`, where:
    - :math:`D` is a diagonal matrix with :math:`D_{ii}` = the sum of the i-th row of A.
    In other words, :math:`D` is the incoming degree of each node.

    With self_layer=True, it is equivalent to the above plus :math:`(H W_{\\text{self}})`, where:
    - :math:`W_{\\text{self}}` is a separate weight matrix to filter each node's self features.

    Note that when self_layer is True, A should not include self-loop.

    Example:
        >>> node_feat = torch.rand(1, 3, 5)
        >>> i = torch.LongTensor(
        ...     [[0, 1, 1, 2, 2, 0], [1, 0, 2, 1, 0, 2]])
        >>> v = torch.FloatTensor([1, 1, 1, 1, 1, 1])
        >>> adj = torch.sparse.FloatTensor(i, v, torch.Size([3, 3]))
        >>> model = GraphConv(5, 10)
        >>> output = model(node_feat, adj)
        >>> # pre-normalize adj
        >>> adj = normalize_adj(adj)
        >>> output = model(node_feat, adj, normalize_adj=False)

    If you use this code, please cite the original paper in addition to Kaolin.
    .. code-block::
        @article{kipf2016semi,
          title={Semi-Supervised Classification with Graph Convolutional Networks},
          author={Kipf, Thomas N and Welling, Max},
          journal={arXiv preprint arXiv:1609.02907},
          year={2016}
        }

    Args:
        input_dim (int): The number of features in each input node.
        output_dim (int): The number of features in each output node.
        bias (bool): Whether to add bias after the node-wise linear layer.
    """

    def __init__(self, input_dim, output_dim, self_layer=True, bias=True):
        super(GraphConv, self).__init__()
        self.self_layer = self_layer
        self.linear = nn.Linear(input_dim, output_dim, bias=bias)
        if self_layer:
            self.linear_self = nn.Linear(input_dim, output_dim, bias=bias)
        else:
            self.linear_self = None
        self.initialize()

    def initialize(self):
        nn.init.xavier_uniform_(self.linear.weight.data)
        if self.linear.bias is not None:
            self.linear.bias.data.uniform_(-1.0, 1.0)
        if self.self_layer:
            nn.init.xavier_uniform_(self.linear_self.weight.data)
            if self.linear_self.bias is not None:
                self.linear_self.bias.data.uniform_(-1.0, 1.0)

    def forward(self, node_feat, adj, normalize_adj=True):
        """
        Args:
            node_feat (torch.FloatTensor):
                Shape = (batch_size, num_nodes, input_dim)
                The input features of each node.
            adj (torch.sparse.FloatTensor or torch.FloatTensor):
                Shape = (num_nodes, num_nodes)
                The adjacency matrix. adj[i, j] is non-zero if there's an
                incoming edge from j to i. Should not include self-loop if
                self_layer is True.
            normalize_adj (bool):
                Set this to true to apply normalization to adjacency; that is,
                each output feature will be divided by the number of incoming
                neighbors. If normalization is not desired, or if the adjacency
                matrix is pre-normalized, set this to False to improve
                performance.

        Returns:
            (torch.FloatTensor):
                The output features of each node.
                Shape = (batch_size, num_nodes, output_dim)
        """
        if adj.type().endswith('sparse.FloatTensor'):
            if normalize_adj:
                norm = torch.sparse.mm(adj, torch.ones((adj.shape[0], 1),
                    device=node_feat.device))
                result = sparse_bmm(adj, self.linear(node_feat)) / norm
            else:
                result = sparse_bmm(adj, self.linear(node_feat))
        elif normalize_adj:
            norm = torch.matmul(adj, torch.ones((adj.shape[0], 1), device=
                node_feat.device))
            result = torch.matmul(adj, self.linear(node_feat)) / norm
        else:
            result = torch.matmul(adj, self.linear(node_feat))
        if self.self_layer:
            result += self.linear_self(node_feat)
        return result


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


def get_init_inputs():
    return [[], {'input_dim': 4, 'output_dim': 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 import nn
import torch.nn
import torch.autograd
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_ones_0(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = 1.0
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused_add_div_1(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x2, xmask)
    tmp4 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 / tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tl.store(in_out_ptr0 + x2, tmp6, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_ones_0[grid(4)](buf0, 4, XBLOCK=4, num_warps=1,
            num_stages=1)
        buf1 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
            buf0, out=buf1)
        del buf0
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_4, reinterpret_tensor(primals_2, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_3, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf2)
        del primals_3
        del primals_4
        buf3 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(primals_1, (16, 4, 4), (16, 4,
            1), 0), reinterpret_tensor(buf2, (16, 4, 4), (16, 4, 1), 0),
            out=buf3)
        buf4 = buf2
        del buf2
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf4)
        del primals_5
        buf5 = reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf3
        triton_poi_fused_add_div_1[grid(256)](buf5, buf1, buf4, primals_6, 
            256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf4
        del primals_6
    return buf5, buf1, reinterpret_tensor(primals_2, (64, 4), (4, 1), 0
        ), reinterpret_tensor(primals_1, (16, 4, 4), (16, 1, 4), 0)


def sparse_bmm(sparse_matrix, dense_matrix_batch):
    """
    Perform torch.bmm on an unbatched sparse matrix and a batched dense matrix.

    Args:
        sparse_matrix (torch.sparse.FloatTensor): Shape = (m, n)
        dense_matrix_batch (torch.FloatTensor): Shape = (b, n, p)

    Returns:
        (torch.FloatTensor):
            Result of the batched matrix multiplication. Shape = (b, n, p)
    """
    m = sparse_matrix.shape[0]
    b, n, p = dense_matrix_batch.shape
    dense_matrix = dense_matrix_batch.transpose(0, 1).reshape(n, b * p)
    result = torch.sparse.mm(sparse_matrix, dense_matrix)
    return result.reshape(m, b, p).transpose(0, 1)


class GraphConvNew(nn.Module):
    """
    A simple graph convolution layer, similar to the one defined in Kipf et al.
    https://arxiv.org/abs/1609.02907

    This operation with self_layer=False is equivalent to :math:`(A H W)` where:
    - :math:`H` is the node features with shape (batch_size, num_nodes, input_dim)
    - :math:`W` is a weight matrix of shape (input_dim, output_dim)
    - :math:`A` is the adjacency matrix of shape (num_nodes, num_nodes).
    It can include self-loop.

    With normalize_adj=True, it is equivalent to :math:`(D^{-1} A H W)`, where:
    - :math:`D` is a diagonal matrix with :math:`D_{ii}` = the sum of the i-th row of A.
    In other words, :math:`D` is the incoming degree of each node.

    With self_layer=True, it is equivalent to the above plus :math:`(H W_{\\text{self}})`, where:
    - :math:`W_{\\text{self}}` is a separate weight matrix to filter each node's self features.

    Note that when self_layer is True, A should not include self-loop.

    Example:
        >>> node_feat = torch.rand(1, 3, 5)
        >>> i = torch.LongTensor(
        ...     [[0, 1, 1, 2, 2, 0], [1, 0, 2, 1, 0, 2]])
        >>> v = torch.FloatTensor([1, 1, 1, 1, 1, 1])
        >>> adj = torch.sparse.FloatTensor(i, v, torch.Size([3, 3]))
        >>> model = GraphConv(5, 10)
        >>> output = model(node_feat, adj)
        >>> # pre-normalize adj
        >>> adj = normalize_adj(adj)
        >>> output = model(node_feat, adj, normalize_adj=False)

    If you use this code, please cite the original paper in addition to Kaolin.
    .. code-block::
        @article{kipf2016semi,
          title={Semi-Supervised Classification with Graph Convolutional Networks},
          author={Kipf, Thomas N and Welling, Max},
          journal={arXiv preprint arXiv:1609.02907},
          year={2016}
        }

    Args:
        input_dim (int): The number of features in each input node.
        output_dim (int): The number of features in each output node.
        bias (bool): Whether to add bias after the node-wise linear layer.
    """

    def __init__(self, input_dim, output_dim, self_layer=True, bias=True):
        super(GraphConvNew, self).__init__()
        self.self_layer = self_layer
        self.linear = nn.Linear(input_dim, output_dim, bias=bias)
        if self_layer:
            self.linear_self = nn.Linear(input_dim, output_dim, bias=bias)
        else:
            self.linear_self = None
        self.initialize()

    def initialize(self):
        nn.init.xavier_uniform_(self.linear.weight.data)
        if self.linear.bias is not None:
            self.linear.bias.data.uniform_(-1.0, 1.0)
        if self.self_layer:
            nn.init.xavier_uniform_(self.linear_self.weight.data)
            if self.linear_self.bias is not None:
                self.linear_self.bias.data.uniform_(-1.0, 1.0)

    def forward(self, input_0, input_1):
        primals_3 = self.linear.weight
        primals_4 = self.linear.bias
        primals_5 = self.linear_self.weight
        primals_6 = self.linear_self.bias
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]
