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I'm working with a library called datamodel-code-generator that has a command datamodel-codegen which can be used to access its internal generate function as a command line tool. The library has 2 main supported formats: jsonschema and openapi, for which it has separate parsers. Following 'raw' parsing by these specific parsers, the base parser does a bunch of intricate routines that I find quite inscrutable, and was wondering if I could get some advice on by showing them to you. These routines have method names beginning with two underscores (I call these 'private' methods). Please read the following code and explain it in summary to me.

def parse(
    self,
    with_import: Optional[bool] = True,
    format_: Optional[bool] = True,
    settings_path: Optional[Path] = None,
) -> Union[str, Dict[Tuple[str, ...], Result]]:
    self.parse_raw()

lmmx

from __future__ import annotations
from dataclasses import dataclass, field
from datetime import date
from dataclass_wizard import JSONWizard, json_field

@dataclass
class Lesson(JSONWizard):
    _def: str = json_field('def', all=True)
    _if: date = json_field('if', all=True)

lmmx

The talk at Google IO 2023 covered several advancements in AI and new features across Google's products and services. Here's a summary:

1. Help Me Write in Gmail: Introducing a feature that uses AI to help users draft emails. Users can request assistance in crafting emails, such as asking for a full refund, and Help Me Write generates a draft using prompts and relevant information from previous emails.

2. Magic Eraser and Magic Editor in Google Photos: AI-powered computational photography tools for photo editing. Magic Eraser removes unwanted distractions, while Magic Editor allows users to make advanced edits like removing objects and adjusting elements in photos.

3. Palm 2 and MedPalm 2: New models in Google's AI lineup. Palm 2 is a highly capable model for various tasks, and MedPalm 2 is fine-tuned on medical knowledge, performing at an expert level on medical licensing exam-style questions.

4. Gemini and Bard: Google's next-generation foundation model, Gemini, designed to be multimodal and enable futu

lmmx

import folium
import numpy as np
import pandas as pd
from python_tsp.exact import solve_tsp_dynamic_programming
from sklearn.metrics import DistanceMetric

cities = ["London", "Paris", "Madrid", "Berlin", "Rome"]
print("Cities:", cities, "\n")
# Source: https://www.kaggle.com/datasets/juanmah/world-cities
df = pd.read_csv("worldcities.csv")

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You can customize the behavior of the AsyncRun plugin to insert the output into the current buffer by using a custom handler function.

Follow these steps to set up the custom handler:

- In your .vimrc, add the following function:

```vim
function! MyAsyncHandler(job, data) abort
  let l:lines = split(a:data, "\n")
  call append(line('$'), l:lines[:-2])

lmmx

class TrieNode(object):
    def __init__(self):
        self.data = {}
        self.is_word = False


class Trie(object):
    def __init__(self):
        self.root = TrieNode()

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import cProfile
import pstats
from pathlib import Path

from dict_lcp_trie_gpt import Trie
from lcp_groups import lcp_trie

n_words = 23
words = Path("wordlist_google-10000-english.txt").read_text().splitlines()[:n_words]

lmmx

first, second = "/a/b/c".lstrip("/").replace("/", "//", 1).split("/", 1)