python

from products.models import Product


def import_products_bulk(product_data):
    """Import thousands of products efficiently."""
    products = [

import pandas as pd
import torch
from PIL import Image
from torch.utils.data import Dataset, DataLoader

class ProductImageDataset(Dataset):

from typing import Optional
from pydantic import BaseModel, EmailStr, Field, field_validator, model_validator


class CreateUserRequest(BaseModel):
    model_config = {"extra": "forbid"}

from rest_framework import serializers
from .models import Author, Book


class BookSerializer(serializers.ModelSerializer):
    class Meta:

import time
import threading
from dataclasses import dataclass, field
from typing import Callable, Dict, List

import time
from dataclasses import dataclass
from typing import Optional

import requests

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.metrics import classification_report

pipeline = Pipeline([

from abc import ABC, abstractmethod


class UnknownChannel(Exception):
    pass

import contextvars
import uuid

_correlation_id: contextvars.ContextVar[str] = contextvars.ContextVar(
    "correlation_id", default="-"
)

from sklearn.compose import ColumnTransformer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.ensemble import RandomForestClassifier

import asyncio
import time
from dataclasses import dataclass, field


@dataclass

import pandas as pd

df = pd.read_csv(
    'orders.csv',
    parse_dates=['created_at'],
    dtype={