mberr

import torch
from torch import nn

class RationalActivation(nn.Module):
    """
    A rational activation function with trainable parameters.
    
    Inspired by https://arxiv.org/abs/2205.01549.
    
    .. seealso::

mberr

"""Several similarity matrix normalization methods."""
import torch


def csls(
    sim: torch.FloatTensor,
    k: Optional[int] = 1,
) -> torch.FloatTensor:
    """
    Apply CSLS normalization to a similarity matrix.

mberr

"""Determine optimal threshold for Macro F1 score."""
from typing import Tuple
import numpy
from sklearn.metrics._ranking import _binary_clf_curve


def f1_scores(
    precision: numpy.ndarray,
    recall: numpy.ndarray,
) -> numpy.ndarray:

mberr

import torch
from torch import nn
import logging

logger = logging.getLogger(__name__)


# pylint: disable=abstract-method
class ExtendedModule(nn.Module):
    """Extends nn.Module by a few utility methods."""

mberr

def latex_bold(text: str) -> str:
    """Format text in bold font using Latex."""
    return rf"\textbf{{{text}}}"


def highlight_max(
    data: pandas.Series,
    float_formatter: Callable[[float], str] = "{:2.2f}".format,
    highlighter: Callable[[str], str] = latex_bold,
) -> pandas.Series:

mberr

import random
import string
from typing import Sequence


def random_sentence_list(
    num_sentences: int = 1,
    word_sep: str = ' ',
    min_num_words: int = 1,
    max_num_words: int = 1,

mberr

"""Find maximal parameter value for a given CUDA device by successive halvening."""
from typing import Callable, Tuple, TypeVar
import torch

R = TypeVar('R')


def maximize_memory_utilization(
    func: Callable[..., R],
    parameter_name: str,