# 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_8/inductor_cache/j5/cj5xkjgnxkrinr2irjagjjwl3tzm5reujidlp4qyrsdkrs7t3nwx.py
# Topologically Sorted Source Nodes: [shifted_color], Original ATen: [aten.sub]
# Source node to ATen node mapping:
#   shifted_color => sub
# Graph fragment:
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg1_1, %unsqueeze), kwargs = {})
triton_poi_fused_sub_0 = async_compile.triton('triton_poi_fused_sub_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=[1024], 
    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_sub_0', '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_sub_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex % 256
    x0 = xindex % 64
    x2 = (xindex // 256)
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x3), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tl.store(out_ptr0 + (x4), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/my/cmy25ejia5o2rat776uwsbvvmsxs5ygvdojzg6lcj5wdlcpt6zvw.py
# Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   matmul => clone
# Graph fragment:
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand_1,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_1 = async_compile.triton('triton_poi_fused_clone_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=[1024], 
    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_clone_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_clone_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 256
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/ol/colz4vorzbhuh2n3nbomhe7g4ya7ygwd5tzzersugg4jd2mtt2bz.py
# Topologically Sorted Source Nodes: [mul, output], Original ATen: [aten.mul, aten.sum]
# Source node to ATen node mapping:
#   mul => mul
#   output => sum_1
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%squeeze_1, %sub), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [2]), kwargs = {})
triton_poi_fused_mul_sum_2 = async_compile.triton('triton_poi_fused_mul_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.pointwise(
    size_hints=[256], 
    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_mul_sum_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, '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_mul_sum_2(in_ptr0, in_ptr1, 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 % 16
    x1 = (xindex // 16)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (64*x1)), xmask)
    tmp2 = tl.load(in_ptr1 + (x0 + (64*x1)), xmask)
    tmp4 = tl.load(in_ptr0 + (16 + x0 + (64*x1)), xmask)
    tmp6 = tl.load(in_ptr1 + (16 + x0 + (64*x1)), xmask)
    tmp9 = tl.load(in_ptr0 + (32 + x0 + (64*x1)), xmask)
    tmp11 = tl.load(in_ptr1 + (32 + x0 + (64*x1)), xmask)
    tmp14 = tl.load(in_ptr0 + (48 + x0 + (64*x1)), xmask)
    tmp16 = tl.load(in_ptr1 + (48 + x0 + (64*x1)), xmask)
    tmp1 = -tmp0
    tmp3 = tmp1 * tmp2
    tmp5 = -tmp4
    tmp7 = tmp5 * tmp6
    tmp8 = tmp3 + tmp7
    tmp10 = -tmp9
    tmp12 = tmp10 * tmp11
    tmp13 = tmp8 + tmp12
    tmp15 = -tmp14
    tmp17 = tmp15 * tmp16
    tmp18 = tmp13 + tmp17
    tl.store(out_ptr0 + (x2), tmp18, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_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))
    assert_size_stride(arg2_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, 4, 4), (256, 64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [shifted_color], Original ATen: [aten.sub]
        stream0 = get_raw_stream(0)
        triton_poi_fused_sub_0.run(arg1_1, arg0_1, buf0, 1024, grid=grid(1024), stream=stream0)
        del arg0_1
        del arg1_1
        buf1 = empty_strided_cuda((4, 4, 1, 4, 4, 4), (256, 64, 64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.clone]
        triton_poi_fused_clone_1.run(arg2_1, buf1, 1024, grid=grid(1024), stream=stream0)
        del arg2_1
        buf2 = empty_strided_cuda((64, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf0, (64, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf1, (64, 4, 4), (16, 4, 1), 0), out=buf2)
        del buf1
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul, output], Original ATen: [aten.mul, aten.sum]
        triton_poi_fused_mul_sum_2.run(buf2, buf0, buf3, 256, grid=grid(256), stream=stream0)
        del buf0
        del buf2
    return (buf3, )


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)
    arg2_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1, arg2_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)

import torch
import torch.nn as nn
import torch.utils.data


class QuadraticForm(torch.autograd.Function):
    """
    This is a custom function that, given two parameters mew and sigma, implements quadratic form. 
    This function takes a representation of a color in vector space and returns a unnormalized score attributed to that color swab.
    """

    @staticmethod
    def forward(ctx, mew, co_var, color):
        """        
        mew : FloatTensor
            m x k matrix, where m is the number of examples, where k is the length of the representation of the color
        co_var: FloatTensor
            m x k x k matrix, sigma in the quadratic form. 
        color: FloatTensor
            m x p x k matrix, where each example has a p vectors of a single color representations of length k
        ------
        outputs:
            m x p matrix of scores.

        """
        ctx.save_for_backward(mew, co_var, color)
        shifted_color = color - mew.unsqueeze(1)
        vec_mat_mult = -torch.matmul(shifted_color.unsqueeze(2), co_var.
            unsqueeze(1)).squeeze(1)
        output = (vec_mat_mult.squeeze(2) * shifted_color).sum(2)
        return output

    @staticmethod
    def backward(ctx, grad_output):
        """
        The derivative of the quadratic form.
        
        input : tuple of FloatTensors
            mew : FloatTensor
                m x k matrix, where m is the number of examples, where k is the length of the representation of the color
            co_var: FloatTensor
                m x k x k matrix, sigma in the quadratic form
            color: FloatTensor
                m x k matrix, where each example has a vector of a single color representation of length k
        output : FloatTensor
            The float tensor for the gradients of a quadratic function
        """
        mew, co_var, color = ctx.saved_tensors
        grad_mean = grad_co_var = grad_color = None
        shifted_color = color - mew.unsqueeze(1)
        if ctx.needs_input_grad[0]:
            grad_mean = torch.matmul(shifted_color.unsqueeze(2), (co_var +
                co_var.permute(0, 2, 1)).unsqueeze(1)).squeeze(2)
            grad_mean = grad_mean * grad_output.unsqueeze(2)
            grad_mean = grad_mean.sum(1)
        if ctx.needs_input_grad[1]:
            grad_co_var = -torch.einsum('bki,bkj->bkij', (shifted_color,
                shifted_color))
            grad_co_var = grad_co_var * grad_output.unsqueeze(2).unsqueeze(3)
            grad_co_var = grad_co_var.sum(1)
        return grad_mean, grad_co_var, grad_color


class AttentionalColorizedListenerDecoder(nn.Module):
    """
    Simple decoder model for the neural literal/pragmatic listener.
    This model takes in two statistical params, mew and sigma, and returns a vector containing the normalized scores
    of each color in the context.
    """

    def __init__(self, device, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.device = device
        self.transform_func = QuadraticForm.apply
        self.hidden_activation = nn.Softmax(dim=1)

    def forward(self, color_seqs, mew, sigma):
        """
        color_seqs : FloatTensor
            A m x k x n tensor where m is the number of examples, k is the number of colors in the context, and
            n is the size of the color dimension after transform
        """
        color_scores = self.transform_func(mew, sigma, color_seqs)
        output = self.hidden_activation(color_scores)
        return output


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


def get_init_inputs():
    return [[], {'device': 0}]

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
import torch.nn as nn
import torch.utils.data
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_sub_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex % 256
    x0 = xindex % 64
    x2 = xindex // 256
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp0 - tmp1
    tl.store(out_ptr0 + x4, tmp2, xmask)


@triton.jit
def triton_poi_fused_clone_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 256
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_mul_sum_2(in_ptr0, in_ptr1, 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 % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
    tmp2 = tl.load(in_ptr1 + (x0 + 64 * x1), xmask)
    tmp4 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
    tmp6 = tl.load(in_ptr1 + (16 + x0 + 64 * x1), xmask)
    tmp9 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tmp11 = tl.load(in_ptr1 + (32 + x0 + 64 * x1), xmask)
    tmp14 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
    tmp16 = tl.load(in_ptr1 + (48 + x0 + 64 * x1), xmask)
    tmp1 = -tmp0
    tmp3 = tmp1 * tmp2
    tmp5 = -tmp4
    tmp7 = tmp5 * tmp6
    tmp8 = tmp3 + tmp7
    tmp10 = -tmp9
    tmp12 = tmp10 * tmp11
    tmp13 = tmp8 + tmp12
    tmp15 = -tmp14
    tmp17 = tmp15 * tmp16
    tmp18 = tmp13 + tmp17
    tl.store(out_ptr0 + x2, tmp18, xmask)


def call(args):
    arg0_1, arg1_1, arg2_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))
    assert_size_stride(arg2_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, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_sub_0[grid(1024)](arg1_1, arg0_1, buf0, 1024,
            XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
        del arg1_1
        buf1 = empty_strided_cuda((4, 4, 1, 4, 4, 4), (256, 64, 64, 16, 4, 
            1), torch.float32)
        triton_poi_fused_clone_1[grid(1024)](arg2_1, buf1, 1024, XBLOCK=256,
            num_warps=4, num_stages=1)
        del arg2_1
        buf2 = empty_strided_cuda((64, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf0, (64, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf1, (64, 4, 4), (16, 4, 1), 0), out=buf2)
        del buf1
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_mul_sum_2[grid(256)](buf2, buf0, buf3, 256, XBLOCK
            =256, num_warps=4, num_stages=1)
        del buf0
        del buf2
    return buf3,


class QuadraticForm(torch.autograd.Function):
    """
    This is a custom function that, given two parameters mew and sigma, implements quadratic form. 
    This function takes a representation of a color in vector space and returns a unnormalized score attributed to that color swab.
    """

    @staticmethod
    def forward(ctx, mew, co_var, color):
        """        
        mew : FloatTensor
            m x k matrix, where m is the number of examples, where k is the length of the representation of the color
        co_var: FloatTensor
            m x k x k matrix, sigma in the quadratic form. 
        color: FloatTensor
            m x p x k matrix, where each example has a p vectors of a single color representations of length k
        ------
        outputs:
            m x p matrix of scores.

        """
        ctx.save_for_backward(mew, co_var, color)
        shifted_color = color - mew.unsqueeze(1)
        vec_mat_mult = -torch.matmul(shifted_color.unsqueeze(2), co_var.
            unsqueeze(1)).squeeze(1)
        output = (vec_mat_mult.squeeze(2) * shifted_color).sum(2)
        return output

    @staticmethod
    def backward(ctx, grad_output):
        """
        The derivative of the quadratic form.
        
        input : tuple of FloatTensors
            mew : FloatTensor
                m x k matrix, where m is the number of examples, where k is the length of the representation of the color
            co_var: FloatTensor
                m x k x k matrix, sigma in the quadratic form
            color: FloatTensor
                m x k matrix, where each example has a vector of a single color representation of length k
        output : FloatTensor
            The float tensor for the gradients of a quadratic function
        """
        mew, co_var, color = ctx.saved_tensors
        grad_mean = grad_co_var = grad_color = None
        shifted_color = color - mew.unsqueeze(1)
        if ctx.needs_input_grad[0]:
            grad_mean = torch.matmul(shifted_color.unsqueeze(2), (co_var +
                co_var.permute(0, 2, 1)).unsqueeze(1)).squeeze(2)
            grad_mean = grad_mean * grad_output.unsqueeze(2)
            grad_mean = grad_mean.sum(1)
        if ctx.needs_input_grad[1]:
            grad_co_var = -torch.einsum('bki,bkj->bkij', (shifted_color,
                shifted_color))
            grad_co_var = grad_co_var * grad_output.unsqueeze(2).unsqueeze(3)
            grad_co_var = grad_co_var.sum(1)
        return grad_mean, grad_co_var, grad_color


class AttentionalColorizedListenerDecoderNew(nn.Module):
    """
    Simple decoder model for the neural literal/pragmatic listener.
    This model takes in two statistical params, mew and sigma, and returns a vector containing the normalized scores
    of each color in the context.
    """

    def __init__(self, device, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.device = device
        self.transform_func = QuadraticForm.apply
        self.hidden_activation = nn.Softmax(dim=1)

    def forward(self, input_0, input_1, input_2):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        output = call([arg0_1, arg1_1, arg2_1])
        return output[0]
