# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_7/inductor_cache/u5/cu5dkuy7wjkpuhlunr4rsoicbkn7l4bdm4dsnk5ifjkfldz6plxs.py
# Topologically Sorted Source Nodes: [sub, abs_1, norm, neg, expnorm, sum_2], Original ATen: [aten.sub, aten.abs, aten.sum, aten.neg, aten.exp]
# Source node to ATen node mapping:
#   abs_1 => abs_1
#   expnorm => exp
#   neg => neg
#   norm => sum_1
#   sub => sub
#   sum_2 => sum_2
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%unsqueeze, %permute), kwargs = {})
#   %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%sub,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%abs_1, [3]), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%sum_1,), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%neg,), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [0]), kwargs = {})
triton_poi_fused_abs_exp_neg_sub_sum_0 = async_compile.triton('triton_poi_fused_abs_exp_neg_sub_sum_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: '*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_abs_exp_neg_sub_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 20, '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_abs_exp_neg_sub_sum_0(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
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (4*x2), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (1 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (2 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr0 + (3 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr0 + (16 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp24 = tl.load(in_ptr0 + (17 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr0 + (18 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp32 = tl.load(in_ptr0 + (19 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp39 = tl.load(in_ptr0 + (32 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp42 = tl.load(in_ptr0 + (33 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp46 = tl.load(in_ptr0 + (34 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp50 = tl.load(in_ptr0 + (35 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp57 = tl.load(in_ptr0 + (48 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp60 = tl.load(in_ptr0 + (49 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp64 = tl.load(in_ptr0 + (50 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp68 = tl.load(in_ptr0 + (51 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp3 = tl_math.abs(tmp2)
    tmp6 = tmp4 - tmp5
    tmp7 = tl_math.abs(tmp6)
    tmp8 = tmp3 + tmp7
    tmp11 = tmp9 - tmp10
    tmp12 = tl_math.abs(tmp11)
    tmp13 = tmp8 + tmp12
    tmp16 = tmp14 - tmp15
    tmp17 = tl_math.abs(tmp16)
    tmp18 = tmp13 + tmp17
    tmp19 = -tmp18
    tmp20 = tl_math.exp(tmp19)
    tmp22 = tmp0 - tmp21
    tmp23 = tl_math.abs(tmp22)
    tmp25 = tmp4 - tmp24
    tmp26 = tl_math.abs(tmp25)
    tmp27 = tmp23 + tmp26
    tmp29 = tmp9 - tmp28
    tmp30 = tl_math.abs(tmp29)
    tmp31 = tmp27 + tmp30
    tmp33 = tmp14 - tmp32
    tmp34 = tl_math.abs(tmp33)
    tmp35 = tmp31 + tmp34
    tmp36 = -tmp35
    tmp37 = tl_math.exp(tmp36)
    tmp38 = tmp20 + tmp37
    tmp40 = tmp0 - tmp39
    tmp41 = tl_math.abs(tmp40)
    tmp43 = tmp4 - tmp42
    tmp44 = tl_math.abs(tmp43)
    tmp45 = tmp41 + tmp44
    tmp47 = tmp9 - tmp46
    tmp48 = tl_math.abs(tmp47)
    tmp49 = tmp45 + tmp48
    tmp51 = tmp14 - tmp50
    tmp52 = tl_math.abs(tmp51)
    tmp53 = tmp49 + tmp52
    tmp54 = -tmp53
    tmp55 = tl_math.exp(tmp54)
    tmp56 = tmp38 + tmp55
    tmp58 = tmp0 - tmp57
    tmp59 = tl_math.abs(tmp58)
    tmp61 = tmp4 - tmp60
    tmp62 = tl_math.abs(tmp61)
    tmp63 = tmp59 + tmp62
    tmp65 = tmp9 - tmp64
    tmp66 = tl_math.abs(tmp65)
    tmp67 = tmp63 + tmp66
    tmp69 = tmp14 - tmp68
    tmp70 = tl_math.abs(tmp69)
    tmp71 = tmp67 + tmp70
    tmp72 = -tmp71
    tmp73 = tl_math.exp(tmp72)
    tmp74 = tmp56 + tmp73
    tl.store(out_ptr0 + (x2), tmp74, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/ow/cowo7zsxbfhomz4ksno5fgpcv6pedpwuunbiu6n5udvebqdjydvk.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   x => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_1, %sub_1], 1), kwargs = {})
triton_poi_fused_cat_1 = async_compile.triton('triton_poi_fused_cat_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[32], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*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_cat_1', '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_cat_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32
    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
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + ((4*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr1 + ((4*x1) + ((-4) + x0)), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
    tmp10 = 1.0
    tmp11 = tmp9 - tmp10
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp6, tmp11, tmp12)
    tmp14 = tl.where(tmp4, tmp5, tmp13)
    tl.store(out_ptr0 + (x2), tmp14, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matrices], Original ATen: [aten.mm]
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (4, 16), (16, 1), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sub, abs_1, norm, neg, expnorm, sum_2], Original ATen: [aten.sub, aten.abs, aten.sum, aten.neg, aten.exp]
        stream0 = get_raw_stream(0)
        triton_poi_fused_abs_exp_neg_sub_sum_0.run(buf0, buf1, 16, grid=grid(16), stream=stream0)
        buf2 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat]
        triton_poi_fused_cat_1.run(primals_1, buf1, buf2, 32, grid=grid(32), stream=stream0)
        del buf1
    return (buf2, buf0, reinterpret_tensor(primals_1, (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), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import torch
import torch.nn as nn
from torch.nn import init


class MiniBatchDiscrimination(nn.Module):
    """
    source: https://gist.github.com/t-ae/732f78671643de97bbe2c46519972491
    paper: Salimans et al. 2016. Improved Methods for Training GANs
    """

    def __init__(self, in_features, out_features, kernel_dims, mean=False):
        super().__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.kernel_dims = kernel_dims
        self.mean = mean
        self.T = nn.Parameter(torch.Tensor(in_features, out_features,
            kernel_dims), requires_grad=True)
        init.normal_(self.T, 0, 1)

    def forward(self, x):
        matrices = x.mm(self.T.view(self.in_features, -1))
        matrices = matrices.view(-1, self.out_features, self.kernel_dims)
        M = matrices.unsqueeze(0)
        M_T = M.permute(1, 0, 2, 3)
        norm = torch.abs(M - M_T).sum(3)
        expnorm = torch.exp(-norm)
        o_b = expnorm.sum(0) - 1
        if self.mean:
            o_b /= x.size(0) - 1
        x = torch.cat([x, o_b], 1)
        return x


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


def get_init_inputs():
    return [[], {'in_features': 4, 'out_features': 4, 'kernel_dims': 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 math as tl_math
import torch.nn as nn
from torch.nn import init
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_abs_exp_neg_sub_sum_0(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
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + 4 * x2, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (1 + 4 * x2), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (2 + 4 * x2), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp14 = tl.load(in_ptr0 + (3 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp15 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp21 = tl.load(in_ptr0 + (16 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp24 = tl.load(in_ptr0 + (17 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp28 = tl.load(in_ptr0 + (18 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp32 = tl.load(in_ptr0 + (19 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp39 = tl.load(in_ptr0 + (32 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp42 = tl.load(in_ptr0 + (33 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp46 = tl.load(in_ptr0 + (34 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp50 = tl.load(in_ptr0 + (35 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp57 = tl.load(in_ptr0 + (48 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp60 = tl.load(in_ptr0 + (49 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp64 = tl.load(in_ptr0 + (50 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp68 = tl.load(in_ptr0 + (51 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp0 - tmp1
    tmp3 = tl_math.abs(tmp2)
    tmp6 = tmp4 - tmp5
    tmp7 = tl_math.abs(tmp6)
    tmp8 = tmp3 + tmp7
    tmp11 = tmp9 - tmp10
    tmp12 = tl_math.abs(tmp11)
    tmp13 = tmp8 + tmp12
    tmp16 = tmp14 - tmp15
    tmp17 = tl_math.abs(tmp16)
    tmp18 = tmp13 + tmp17
    tmp19 = -tmp18
    tmp20 = tl_math.exp(tmp19)
    tmp22 = tmp0 - tmp21
    tmp23 = tl_math.abs(tmp22)
    tmp25 = tmp4 - tmp24
    tmp26 = tl_math.abs(tmp25)
    tmp27 = tmp23 + tmp26
    tmp29 = tmp9 - tmp28
    tmp30 = tl_math.abs(tmp29)
    tmp31 = tmp27 + tmp30
    tmp33 = tmp14 - tmp32
    tmp34 = tl_math.abs(tmp33)
    tmp35 = tmp31 + tmp34
    tmp36 = -tmp35
    tmp37 = tl_math.exp(tmp36)
    tmp38 = tmp20 + tmp37
    tmp40 = tmp0 - tmp39
    tmp41 = tl_math.abs(tmp40)
    tmp43 = tmp4 - tmp42
    tmp44 = tl_math.abs(tmp43)
    tmp45 = tmp41 + tmp44
    tmp47 = tmp9 - tmp46
    tmp48 = tl_math.abs(tmp47)
    tmp49 = tmp45 + tmp48
    tmp51 = tmp14 - tmp50
    tmp52 = tl_math.abs(tmp51)
    tmp53 = tmp49 + tmp52
    tmp54 = -tmp53
    tmp55 = tl_math.exp(tmp54)
    tmp56 = tmp38 + tmp55
    tmp58 = tmp0 - tmp57
    tmp59 = tl_math.abs(tmp58)
    tmp61 = tmp4 - tmp60
    tmp62 = tl_math.abs(tmp61)
    tmp63 = tmp59 + tmp62
    tmp65 = tmp9 - tmp64
    tmp66 = tl_math.abs(tmp65)
    tmp67 = tmp63 + tmp66
    tmp69 = tmp14 - tmp68
    tmp70 = tl_math.abs(tmp69)
    tmp71 = tmp67 + tmp70
    tmp72 = -tmp71
    tmp73 = tl_math.exp(tmp72)
    tmp74 = tmp56 + tmp73
    tl.store(out_ptr0 + x2, tmp74, xmask)


@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 32
    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 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = 1.0
    tmp11 = tmp9 - tmp10
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp6, tmp11, tmp12)
    tmp14 = tl.where(tmp4, tmp5, tmp13)
    tl.store(out_ptr0 + x2, tmp14, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (4, 16),
            (16, 1), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_abs_exp_neg_sub_sum_0[grid(16)](buf0, buf1, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        triton_poi_fused_cat_1[grid(32)](primals_1, buf1, buf2, 32, XBLOCK=
            32, num_warps=1, num_stages=1)
        del buf1
    return buf2, buf0, reinterpret_tensor(primals_1, (4, 4), (1, 4), 0)


class MiniBatchDiscriminationNew(nn.Module):
    """
    source: https://gist.github.com/t-ae/732f78671643de97bbe2c46519972491
    paper: Salimans et al. 2016. Improved Methods for Training GANs
    """

    def __init__(self, in_features, out_features, kernel_dims, mean=False):
        super().__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.kernel_dims = kernel_dims
        self.mean = mean
        self.T = nn.Parameter(torch.Tensor(in_features, out_features,
            kernel_dims), requires_grad=True)
        init.normal_(self.T, 0, 1)

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