polvalente

# To-Do app

run = fn run_fn, state ->
  case IO.gets("Choose one of the following options:\n1. List TO-DOs\n2. Add a new TO-DO\n3. Mark TO-DO as completed\n4. Delete TO-DO.\n5. Exit\n>> ") |> String.trim() do
    "1" ->
      state.to_dos 
      |> Enum.reverse()
      |> Enum.with_index()
      |> Enum.map_join("\n", fn {%{todo: todo, completed: completed}, idx} -> 
        "#{idx}. #{todo} [#{if(completed, do: "✔", else: " ")}]"

polvalente

# To-Do app
Mix.install [:exla, {:nx, github: "elixir-nx/nx", sparse: "nx", override: true}]

max_todo_length = 5
max_todos = 10

run = fn run_fn, state ->
  case IO.gets("Choose one of the following options:\n1. List TO-DOs\n2. Add a new TO-DO\n3. Mark TO-DO as completed\n4. Delete TO-DO.\n5. Exit\n>> ") |> String.trim() do
    "1" ->
      todos = if state.count > 0, do: Nx.to_list(state.to_dos[0..(state.count - 1)]), else: []

polvalente

#include <cmath>
#include <iostream>
#include <vector>

const uint64_t MAX_UINT64 = std::numeric_limits<uint64_t>::max();

uint64_t numDigits(std::vector<uint64_t> num) {
  // num is described by num[0] + num[1] * MAX_UINT64 + num[2] * MAX_UINT64^2 + ...
  if (num.empty()) return 0;

polvalente

In [1]: import torch

In [2]: a = torch.arange(3, 4, 5)

In [3]: a = torch.arange(60).reshape(3, 4, 5)

In [4]: b = torch.arange(24).reshape(1, 4, 3, 2)
# Aqui, (junto com o output da linha Out[7]) a gente vê que 
# que o tensordot usando contraction axes específicas tem
# esse resultado.