usamec

import torch

class ResumableRandomSampler(torch.utils.data.Sampler):
    r"""Samples elements randomly. If without replacement, then sample from a shuffled dataset.
    If with replacement, then user can specify :attr:`num_samples` to draw.
    Arguments:
        data_source (Dataset): dataset to sample from
        replacement (bool): samples are drawn on-demand with replacement if ``True``, default=``False``
        num_samples (int): number of samples to draw, default=`len(dataset)`. This argument
            is supposed to be specified only when `replacement` is ``True``.

usamec

usamec

package com.example

import scala.annotation.tailrec
import scala.collection.immutable.Queue

case class TrieNode[Char, Value](values: List[Value], next: Map[Char, TrieNode[Char, Value]]) {
  def add(str: Seq[Char], value: Value): TrieNode[Char, Value] = {
    @tailrec
    def addWalkDown(node: TrieNode[Char, Value], stack: List[(Char, TrieNode[Char, Value])], remaining: List[Char], value: Value): TrieNode[Char, Value] = {
      remaining match {