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from typing import List
from itertools import chain
import torch
from torch import nn


class MLP(nn.Module):
    def __init__(self,
                hidden_sizes: List[int],
                act_fn: nn.Module = nn.ReLU,

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# http://proceedings.mlr.press/v80/wehrmann18a/wehrmann18a.pdf

from collections import OrderedDict
from typing import Tuple

import torch
from torch import nn
from torch.nn import functional as F

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\documentclass[border=1pt,tikz]{standalone}

\usepackage{listofitems}
\usepackage{tikz}
\usetikzlibrary{arrows}
\usetikzlibrary{backgrounds}

\newcounter{attributecount}
\NewDocumentCommand{\attribute}{mm} {
    \draw[black, fill={#1}, rounded corners=6, thick] (0.1, \value{attributecount}+0.12) rectangle (3.9, \value{attributecount}+0.98) node at (2, \value{attributecount}+0.5) {#2};

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"""
* A simple multi-head classifier implementation.
                                                    +-----------------+
    +--------------+                                |   logits_dict   |
    |    hidden    |                                +-----------------+
    +------+-------+                                         ^
           |                                                 |
           v                            +--------------------+--------------------+
    +--------------+                    |                    |                    |
    | joint_layer  |            +---------------+    +---------------+    +---------------+

lzqlzzq

from typing import List
from itertools import chain
import torch
from torch import nn


class Transpose(nn.Module):
    def __init__(self, *args):
        super().__init__()

lzqlzzq

from typing import List
from itertools import chain
import torch
from torch import nn


class Transpose(nn.Module):
    def __init__(self, *args):
        super().__init__()

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# https://arxiv.org/pdf/2102.12994.pdf

from typing import Dict
from itertools import permutations
import torch
from torch import nn
from math import sqrt


class FMFM(nn.Module):

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# https://arxiv.org/pdf/1810.11921.pdf
from typing import Dict
from itertools import product
import torch
from torch import nn
import torch.nn.functional as F


class AutoInt(nn.Module):
    def __init__(self,

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import torch
import torch.nn as nn
import torch.nn.functional as F
import torchaudio
import numpy as np


def get_fourier_basis(win_length, window_func=torch.hann_window):
    # Create kernels for STFT, initialized to Fourier basis
    n_basis = win_length // 2 + 1

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#ifndef _META_NUMERIC_
#define _META_NUMERIC_

#include <iostream>
#include <string>


namespace metanumeric {

#define UNARY_ASGN_OPTS          \