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

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


# kernel path: runs/run_shard_4/inductor_cache/f7/cf7j4pwlu55ah6fc4huqphitmbwk5lpjrsflp3g4iowoictloibu.py
# Topologically Sorted Source Nodes: [alpha, strength], Original ATen: [aten.add, aten.sum]
# Source node to ATen node mapping:
#   alpha => add
#   strength => sum_1
# Graph fragment:
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%arg0_1, 1.0), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%add, [-1]), kwargs = {})
triton_poi_fused_add_sum_0 = async_compile.triton('triton_poi_fused_add_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_sum_0', 'mutated_arg_names': [], '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_sum_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp1 = 1.0
    tmp2 = tmp0 + tmp1
    tmp4 = tmp3 + tmp1
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 + tmp1
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 + tmp1
    tmp11 = tmp8 + tmp10
    tl.store(out_ptr0 + (x0), tmp11, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ng/cng3qs5i7l7nydt5poohpevomfjvgc4tpjnfgxynhzf2fuov5xde.py
# Topologically Sorted Source Nodes: [alpha], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   alpha => add
# Graph fragment:
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%arg0_1, 1.0), kwargs = {})
triton_poi_fused_add_1 = async_compile.triton('triton_poi_fused_add_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: '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_add_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    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 = 1.0
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x0), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/vn/cvnzeqhjxijtgpwsm3a57xbuknnkhkk7k3udzcqfvczkfyomgkmt.py
# Topologically Sorted Source Nodes: [sub, mul, loss, mean], Original ATen: [aten.sub, aten.mul, aten.sum, aten.mean]
# Source node to ATen node mapping:
#   loss => sum_2
#   mean => mean
#   mul => mul
#   sub => sub
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%unsqueeze, %digamma_1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg1_1, %sub), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [-1]), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sum_2,), kwargs = {})
triton_per_fused_mean_mul_sub_sum_2 = async_compile.triton('triton_per_fused_mean_mul_sub_sum_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.persistent_reduction(
    size_hints=[1, 64],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: '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': {4: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 5), equal_to_1=(4,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_mean_mul_sub_sum_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 12, 'num_reduction': 1, '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}
)
@triton.jit
def triton_per_fused_mean_mul_sub_sum_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r3 = rindex
    r0 = rindex % 4
    r2 = (rindex // 16)
    tmp0 = tl.load(in_ptr0 + (4*r3), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + ((4*r0) + (16*r2)), None, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + (4*r3), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (1 + (4*r3)), None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (1 + (4*r0) + (16*r2)), None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr2 + (1 + (4*r3)), None, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (2 + (4*r3)), None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (2 + (4*r0) + (16*r2)), None, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr2 + (2 + (4*r3)), None, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr0 + (3 + (4*r3)), None, eviction_policy='evict_last')
    tmp18 = tl.load(in_ptr1 + (3 + (4*r0) + (16*r2)), None, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr2 + (3 + (4*r3)), None, eviction_policy='evict_last')
    tmp3 = tmp1 - tmp2
    tmp4 = tmp0 * tmp3
    tmp8 = tmp6 - tmp7
    tmp9 = tmp5 * tmp8
    tmp10 = tmp4 + tmp9
    tmp14 = tmp12 - tmp13
    tmp15 = tmp11 * tmp14
    tmp16 = tmp10 + tmp15
    tmp20 = tmp18 - tmp19
    tmp21 = tmp17 * tmp20
    tmp22 = tmp16 + tmp21
    tmp23 = tl.broadcast_to(tmp22, [XBLOCK, RBLOCK])
    tmp25 = tl.sum(tmp23, 1)[:, None]
    tmp26 = 64.0
    tmp27 = tmp25 / tmp26
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp27, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [alpha, strength], Original ATen: [aten.add, aten.sum]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_sum_0.run(arg0_1, buf0, 64, grid=grid(64), stream=stream0)
        # Topologically Sorted Source Nodes: [alpha, strength, digamma], Original ATen: [aten.add, aten.sum, aten.digamma]
        buf1 = torch.ops.aten.digamma.default(buf0)
        del buf0
        buf2 = buf1
        del buf1
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [alpha], Original ATen: [aten.add]
        triton_poi_fused_add_1.run(arg0_1, buf3, 256, grid=grid(256), stream=stream0)
        del arg0_1
        # Topologically Sorted Source Nodes: [alpha, digamma_1], Original ATen: [aten.add, aten.digamma]
        buf4 = torch.ops.aten.digamma.default(buf3)
        del buf3
        buf5 = buf4
        del buf4
        buf7 = empty_strided_cuda((), (), torch.float32)
        buf8 = buf7; del buf7  # reuse
        # Topologically Sorted Source Nodes: [sub, mul, loss, mean], Original ATen: [aten.sub, aten.mul, aten.sum, aten.mean]
        triton_per_fused_mean_mul_sub_sum_2.run(buf8, arg1_1, buf2, buf5, 1, 64, grid=grid(1), stream=stream0)
        del arg1_1
        del buf2
        del buf5
    return (buf8, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1])
    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)

from torch.nn import Module
import torch
import torch.utils.data
import torch.nn.functional
import torch.autograd


class CrossEntropyBayesRisk(Module):
    """
    <a id="CrossEntropyBayesRisk"></a>

    ## Bayes Risk with Cross Entropy Loss

    Bayes risk is the overall maximum cost of making incorrect estimates.
    It takes a cost function that gives the cost of making an incorrect estimate
    and sums it over all possible outcomes based on probability distribution.

    Here the cost function is cross-entropy loss, for one-hot coded $\\mathbf{y}$
    $$\\sum_{k=1}^K -y_k \\log p_k$$

    We integrate this cost over all $\\mathbf{p}$

    egin{align}
    \\mathcal{L}(\\Theta)
    &= -\\log \\Bigg(
     \\int
      \\Big[ \\sum_{k=1}^K -y_k \\log p_k \\Big]
      rac{1}{B(	extcolor{orange}{\\mathbf{lpha}})}
      \\prod_{k=1}^K p_k^{	extcolor{orange}{lpha_k} - 1}
     d\\mathbf{p}
     \\Bigg ) \\
    &= \\sum_{k=1}^K y_k igg( \\psi(S) - \\psi( 	extcolor{orange}{lpha_k} ) igg)
    \\end{align}

    where $\\psi(\\cdot)$ is the $digamma$ function.
    """

    def forward(self, evidence: 'torch.Tensor', target: 'torch.Tensor'):
        """
        * `evidence` is $\\mathbf{e} \\ge 0$ with shape `[batch_size, n_classes]`
        * `target` is $\\mathbf{y}$ with shape `[batch_size, n_classes]`
        """
        alpha = evidence + 1.0
        strength = alpha.sum(dim=-1)
        loss = (target * (torch.digamma(strength)[:, None] - torch.digamma(
            alpha))).sum(dim=-1)
        return loss.mean()


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


def get_init_inputs():
    return [[], {}]

import torch
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.nn import Module
import torch.utils.data
import torch.nn.functional
import torch.autograd
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_add_sum_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp1 = 1.0
    tmp2 = tmp0 + tmp1
    tmp4 = tmp3 + tmp1
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 + tmp1
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 + tmp1
    tmp11 = tmp8 + tmp10
    tl.store(out_ptr0 + x0, tmp11, xmask)


@triton.jit
def triton_poi_fused_add_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    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 = 1.0
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_per_fused_mean_mul_sub_sum_2(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r3 = rindex
    r0 = rindex % 4
    r2 = rindex // 16
    tmp0 = tl.load(in_ptr0 + 4 * r3, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (4 * r0 + 16 * r2), None, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr2 + 4 * r3, None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (1 + 4 * r3), None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (1 + 4 * r0 + 16 * r2), None, eviction_policy=
        'evict_last')
    tmp7 = tl.load(in_ptr2 + (1 + 4 * r3), None, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (2 + 4 * r3), None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (2 + 4 * r0 + 16 * r2), None, eviction_policy
        ='evict_last')
    tmp13 = tl.load(in_ptr2 + (2 + 4 * r3), None, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr0 + (3 + 4 * r3), None, eviction_policy='evict_last')
    tmp18 = tl.load(in_ptr1 + (3 + 4 * r0 + 16 * r2), None, eviction_policy
        ='evict_last')
    tmp19 = tl.load(in_ptr2 + (3 + 4 * r3), None, eviction_policy='evict_last')
    tmp3 = tmp1 - tmp2
    tmp4 = tmp0 * tmp3
    tmp8 = tmp6 - tmp7
    tmp9 = tmp5 * tmp8
    tmp10 = tmp4 + tmp9
    tmp14 = tmp12 - tmp13
    tmp15 = tmp11 * tmp14
    tmp16 = tmp10 + tmp15
    tmp20 = tmp18 - tmp19
    tmp21 = tmp17 * tmp20
    tmp22 = tmp16 + tmp21
    tmp23 = tl.broadcast_to(tmp22, [XBLOCK, RBLOCK])
    tmp25 = tl.sum(tmp23, 1)[:, None]
    tmp26 = 64.0
    tmp27 = tmp25 / tmp26
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp27, None)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_sum_0[grid(64)](arg0_1, buf0, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf1 = torch.ops.aten.digamma.default(buf0)
        del buf0
        buf2 = buf1
        del buf1
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_1[grid(256)](arg0_1, buf3, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del arg0_1
        buf4 = torch.ops.aten.digamma.default(buf3)
        del buf3
        buf5 = buf4
        del buf4
        buf7 = empty_strided_cuda((), (), torch.float32)
        buf8 = buf7
        del buf7
        triton_per_fused_mean_mul_sub_sum_2[grid(1)](buf8, arg1_1, buf2,
            buf5, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg1_1
        del buf2
        del buf5
    return buf8,


class CrossEntropyBayesRiskNew(Module):
    """
    <a id="CrossEntropyBayesRisk"></a>

    ## Bayes Risk with Cross Entropy Loss

    Bayes risk is the overall maximum cost of making incorrect estimates.
    It takes a cost function that gives the cost of making an incorrect estimate
    and sums it over all possible outcomes based on probability distribution.

    Here the cost function is cross-entropy loss, for one-hot coded $\\mathbf{y}$
    $$\\sum_{k=1}^K -y_k \\log p_k$$

    We integrate this cost over all $\\mathbf{p}$

    egin{align}
    \\mathcal{L}(\\Theta)
    &= -\\log \\Bigg(
     \\int
      \\Big[ \\sum_{k=1}^K -y_k \\log p_k \\Big]
      rac{1}{B(	extcolor{orange}{\\mathbf{lpha}})}
      \\prod_{k=1}^K p_k^{	extcolor{orange}{lpha_k} - 1}
     d\\mathbf{p}
     \\Bigg ) \\
    &= \\sum_{k=1}^K y_k igg( \\psi(S) - \\psi( 	extcolor{orange}{lpha_k} ) igg)
    \\end{align}

    where $\\psi(\\cdot)$ is the $digamma$ function.
    """

    def forward(self, input_0, input_1):
        arg0_1 = input_0
        arg1_1 = input_1
        output = call([arg0_1, arg1_1])
        return output[0]
