# 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_1/inductor_cache/lg/clgvalhvo7ygyvwhczo52ybtcvk6b7yiltaj5x7jlylvvui4lb63.py
# Topologically Sorted Source Nodes: [batch_edge_idcs], Original ATen: [aten.zeros]
# Source node to ATen node mapping:
#   batch_edge_idcs => full
# Graph fragment:
#   %full : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([4, 4], 0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
triton_poi_fused_zeros_0 = async_compile.triton('triton_poi_fused_zeros_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_zeros_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_zeros_0(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 = 0.0
    tl.store(out_ptr0 + (x0), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/zp/czp73p6psv7ch6fl3kwwz2ybx3birwwyi2azbsa6qs42ju5qealv.py
# Topologically Sorted Source Nodes: [eq, batch_edge_idcs_2, row_sum, inv, setitem], Original ATen: [aten.eq, aten._to_copy, aten.sum, aten.reciprocal, aten.mul, aten.lift_fresh, aten.index_put]
# Source node to ATen node mapping:
#   batch_edge_idcs_2 => convert_element_type
#   eq => eq
#   inv => mul, reciprocal
#   row_sum => sum_1
#   setitem => full_default, index_put
# Graph fragment:
#   %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%permute_11, 2.0), kwargs = {})
#   %convert_element_type : [num_users=2] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%eq, torch.float32), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%convert_element_type, [1]), kwargs = {})
#   %reciprocal : [num_users=1] = call_function[target=torch.ops.aten.reciprocal.default](args = (%sum_1,), kwargs = {})
#   %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%reciprocal, 1.0), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cpu, pin_memory: False})
#   %index_put : [num_users=1] = call_function[target=torch.ops.aten.index_put_.default](args = (%mul, [%isinf], %full_default), kwargs = {})
triton_poi_fused__to_copy_eq_index_put_lift_fresh_mul_reciprocal_sum_1 = async_compile.triton('triton_poi_fused__to_copy_eq_index_put_lift_fresh_mul_reciprocal_sum_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=[4], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_eq_index_put_lift_fresh_mul_reciprocal_sum_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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__to_copy_eq_index_put_lift_fresh_mul_reciprocal_sum_1(in_ptr0, 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 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp1 = 2.0
    tmp2 = tmp0 == tmp1
    tmp3 = tmp2.to(tl.float32)
    tmp5 = tmp4 == tmp1
    tmp6 = tmp5.to(tl.float32)
    tmp7 = tmp3 + tmp6
    tmp9 = tmp8 == tmp1
    tmp10 = tmp9.to(tl.float32)
    tmp11 = tmp7 + tmp10
    tmp13 = tmp12 == tmp1
    tmp14 = tmp13.to(tl.float32)
    tmp15 = tmp11 + tmp14
    tmp16 = tl.full([1], 1, tl.int32)
    tmp17 = tmp16 / tmp15
    tmp18 = 1.0
    tmp19 = tmp17 * tmp18
    tmp20 = libdevice.isinf(tmp19).to(tl.int1)
    tmp21 = 0.0
    tmp22 = tl.where(tmp20, tmp21, tmp19)
    tl.store(out_ptr0 + (x0), tmp22, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/qz/cqzvcq6hyo636fyn6n4po46nua32cearyebor3w5tumkhdovphzr.py
# Topologically Sorted Source Nodes: [eq, batch_edge_idcs_2, batch_edge_idcs_3], Original ATen: [aten.eq, aten._to_copy, aten.mul]
# Source node to ATen node mapping:
#   batch_edge_idcs_2 => convert_element_type
#   batch_edge_idcs_3 => mul_1
#   eq => eq
# Graph fragment:
#   %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%permute_11, 2.0), kwargs = {})
#   %convert_element_type : [num_users=2] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%eq, torch.float32), kwargs = {})
#   %mul_1 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convert_element_type, %view_1), kwargs = {})
triton_poi_fused__to_copy_eq_mul_2 = async_compile.triton('triton_poi_fused__to_copy_eq_mul_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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_eq_mul_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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__to_copy_eq_mul_2(in_out_ptr0, in_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_out_ptr0 + (x2), xmask)
    tmp4 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp1 = 2.0
    tmp2 = tmp0 == tmp1
    tmp3 = tmp2.to(tl.float32)
    tmp5 = tmp3 * tmp4
    tl.store(in_out_ptr0 + (x2), tmp5, 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, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 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, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [batch_edge_idcs], Original ATen: [aten.zeros]
        stream0 = get_raw_stream(0)
        triton_poi_fused_zeros_0.run(buf0, 16, grid=grid(16), stream=stream0)
        # Topologically Sorted Source Nodes: [batch_edge_idcs], Original ATen: [aten._sparse_addmm]
        buf1 = torch.ops.aten._sparse_addmm.default(reinterpret_tensor(buf0, (4, 4), (1, 4), 0), primals_2, primals_1, beta=0)
        del primals_1
        del primals_2
        buf2 = buf1
        del buf1
        # Topologically Sorted Source Nodes: [_sparse_mm_1], Original ATen: [aten._sparse_addmm]
        buf3 = torch.ops.aten._sparse_addmm.default(reinterpret_tensor(buf0, (4, 4), (1, 4), 0), buf2, primals_3, beta=0)
        del primals_3
        buf4 = buf3
        del buf3
        buf5 = empty_strided_cuda((4, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [eq, batch_edge_idcs_2, row_sum, inv, setitem], Original ATen: [aten.eq, aten._to_copy, aten.sum, aten.reciprocal, aten.mul, aten.lift_fresh, aten.index_put]
        triton_poi_fused__to_copy_eq_index_put_lift_fresh_mul_reciprocal_sum_1.run(buf4, buf5, 4, grid=grid(4), stream=stream0)
        buf6 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [eq, batch_edge_idcs_2, batch_edge_idcs_3], Original ATen: [aten.eq, aten._to_copy, aten.mul]
        triton_poi_fused__to_copy_eq_mul_2.run(buf6, buf5, 16, grid=grid(16), stream=stream0)
        del buf5
        buf7 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.addmm(primals_6, primals_5, primals_4, alpha=1, beta=1, out=buf7)
        del primals_4
        del primals_6
        buf8 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [h], Original ATen: [aten.mm]
        extern_kernels.mm(buf6, buf7, out=buf8)
        del buf7
    return (buf8, reinterpret_tensor(buf6, (4, 4), (1, 4), 0), reinterpret_tensor(primals_5, (4, 4), (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, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4), (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, 4), (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 math
import torch
import torch.nn as nn
import torch.sparse as sp


class PatchToPatchEdgeConvolution(nn.Module):

    def __init__(self, in_features, out_features):
        super(PatchToPatchEdgeConvolution, self).__init__()
        self.weight = nn.parameter.Parameter(torch.FloatTensor(in_features,
            out_features))
        self.bias = nn.parameter.Parameter(torch.FloatTensor(out_features))
        self.reset_parameters()

    def reset_parameters(self):
        stdv = 1.0 / math.sqrt(self.weight.size(1))
        self.weight.data.uniform_(-stdv, stdv)
        if self.bias is not None:
            self.bias.data.uniform_(-stdv, stdv)

    def forward(self, edge_nodes, adj_matrix, inc_matrix, edge_feats):
        """
        :param edge_nodes: Matrix indicating the nodes which each edge in the
        batch connects. Shape [B, N]
        :param adj_matrix: Sparse adjacency matrix of the graph of shape
        [N, N]. Must contain only 1-entries (i.e. should not be normalised).
        :param inc_matrix: Sparse incidence matrix of the graph of shape
        [N, E].
        :param edge_feats: Features of *all* edges in the graph. Shape [E, D].
        :return: Hidden representation of shape [B, K].
        """
        batch_edge_idcs = sp.mm(adj_matrix.transpose(1, 0), edge_nodes.
            transpose(1, 0))
        batch_edge_idcs = sp.mm(inc_matrix.transpose(1, 0), batch_edge_idcs
            ).transpose(1, 0)
        batch_edge_idcs = (batch_edge_idcs == 2.0).float()
        row_sum = torch.sum(batch_edge_idcs, dim=1)
        inv = 1.0 / row_sum
        inv[torch.isinf(inv)] = 0.0
        batch_edge_idcs = batch_edge_idcs * inv.view(-1, 1)
        h_edges = torch.mm(edge_feats, self.weight) + self.bias
        h = torch.spmm(batch_edge_idcs, h_edges)
        return h


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


def get_init_inputs():
    return [[], {'in_features': 4, 'out_features': 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.triton_helpers import libdevice
import 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_zeros_0(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 = 0.0
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused__to_copy_eq_index_put_lift_fresh_mul_reciprocal_sum_1(
    in_ptr0, 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 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp1 = 2.0
    tmp2 = tmp0 == tmp1
    tmp3 = tmp2.to(tl.float32)
    tmp5 = tmp4 == tmp1
    tmp6 = tmp5.to(tl.float32)
    tmp7 = tmp3 + tmp6
    tmp9 = tmp8 == tmp1
    tmp10 = tmp9.to(tl.float32)
    tmp11 = tmp7 + tmp10
    tmp13 = tmp12 == tmp1
    tmp14 = tmp13.to(tl.float32)
    tmp15 = tmp11 + tmp14
    tmp16 = tl.full([1], 1, tl.int32)
    tmp17 = tmp16 / tmp15
    tmp18 = 1.0
    tmp19 = tmp17 * tmp18
    tmp20 = libdevice.isinf(tmp19).to(tl.int1)
    tmp21 = 0.0
    tmp22 = tl.where(tmp20, tmp21, tmp19)
    tl.store(out_ptr0 + x0, tmp22, xmask)


@triton.jit
def triton_poi_fused__to_copy_eq_mul_2(in_out_ptr0, in_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_out_ptr0 + x2, xmask)
    tmp4 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp1 = 2.0
    tmp2 = tmp0 == tmp1
    tmp3 = tmp2.to(tl.float32)
    tmp5 = tmp3 * tmp4
    tl.store(in_out_ptr0 + x2, tmp5, 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, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 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, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_zeros_0[grid(16)](buf0, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        buf1 = torch.ops.aten._sparse_addmm.default(reinterpret_tensor(buf0,
            (4, 4), (1, 4), 0), primals_2, primals_1, beta=0)
        del primals_1
        del primals_2
        buf2 = buf1
        del buf1
        buf3 = torch.ops.aten._sparse_addmm.default(reinterpret_tensor(buf0,
            (4, 4), (1, 4), 0), buf2, primals_3, beta=0)
        del primals_3
        buf4 = buf3
        del buf3
        buf5 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_poi_fused__to_copy_eq_index_put_lift_fresh_mul_reciprocal_sum_1[
            grid(4)](buf4, buf5, 4, XBLOCK=4, num_warps=1, num_stages=1)
        buf6 = buf4
        del buf4
        triton_poi_fused__to_copy_eq_mul_2[grid(16)](buf6, buf5, 16, XBLOCK
            =16, num_warps=1, num_stages=1)
        del buf5
        buf7 = buf2
        del buf2
        extern_kernels.addmm(primals_6, primals_5, primals_4, alpha=1, beta
            =1, out=buf7)
        del primals_4
        del primals_6
        buf8 = buf0
        del buf0
        extern_kernels.mm(buf6, buf7, out=buf8)
        del buf7
    return buf8, reinterpret_tensor(buf6, (4, 4), (1, 4), 0
        ), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0)


class PatchToPatchEdgeConvolutionNew(nn.Module):

    def __init__(self, in_features, out_features):
        super(PatchToPatchEdgeConvolutionNew, self).__init__()
        self.weight = nn.parameter.Parameter(torch.FloatTensor(in_features,
            out_features))
        self.bias = nn.parameter.Parameter(torch.FloatTensor(out_features))
        self.reset_parameters()

    def reset_parameters(self):
        stdv = 1.0 / math.sqrt(self.weight.size(1))
        self.weight.data.uniform_(-stdv, stdv)
        if self.bias is not None:
            self.bias.data.uniform_(-stdv, stdv)

    def forward(self, input_0, input_1, input_2, input_3):
        primals_1 = self.weight
        primals_6 = self.bias
        primals_2 = input_0
        primals_3 = input_1
        primals_4 = input_2
        primals_5 = input_3
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]
