Question

使用此代码时出现错误，错误是

“ FluentWait类型的具有Timeout（Duration）的方法不是适用于参数（int，TimeUnit）”

Wait wait = new FluentWait(driver)    
    .withTimeout(30, SECONDS)    
    .pollingEvery(5, SECONDS)   
    .ignoring(NoSuchElementException.class);

Answer 1

现在这是正确的用法。

Wait wait = new FluentWait(driver).withTimeout(Duration.ofSeconds(30)).pollingEvery(Duration.ofSeconds(30))
                    .ignoring(NoSuchElementException.class);

Answer 2

使用上述语法，我也遇到相同的错误-

package selenium.SeleniumCourse;
import java.time.Duration;
import java.util.NoSuchElementException;
import org.openqa.selenium.By;
import org.openqa.selenium.WebDriver;
import org.openqa.selenium.chrome.ChromeDriver;
import org.openqa.selenium.support.ui.FluentWait;
import org.openqa.selenium.support.ui.Wait;
import org.openqa.selenium.support.ui.WebDriverWait;

import io.github.bonigarcia.wdm.WebDriverManager;

public class FluentWaitTest {

    public static void main(String[] args) {
        WebDriver driver;
        WebDriverManager.chromedriver().setup();
        driver = new ChromeDriver();
        driver.get("https://the-internet.herokuapp.com/dynamic_loading/1");
        driver.findElement(By.cssSelector("[id='start'] button")).click();

        Wait<WebDriver> wait = new FluentWait<WebDriver>(driver).withTimeout(Duration.ofSeconds(30))                .pollingEvery(Duration.ofSeconds(3))
.ignoring(NoSuchElementException.class);

    }

}

Answer 3

我进行了搜索，以下代码对我有用

def parse_proto(example_proto):
  features = {
    'X': tf.FixedLenFeature((345,), tf.float32),
    'y': tf.FixedLenFeature((5,), tf.float32),
  }
  parsed_features = tf.parse_single_example(example_proto, features)
  return parsed_features['X'], parsed_features['y']

def read_tfrecords(file_names=("file1.tfrecord", "file2.tfrecord", "file3.tfrecord"),
                   buffer_size=10000,
                   batch_size=100):
  dataset = tf.contrib.data.TFRecordDataset(file_names)
  dataset = dataset.map(parse_proto)
  dataset = dataset.shuffle(buffer_size)
  dataset = dataset.repeat()
  dataset = dataset.batch(batch_size)
  return tf.contrib.data.Iterator.from_structure(dataset.output_types, dataset.output_shapes)

fluentwait <webdriver>类型的withtimeout（duration）方法不适用于参数（int，timeunit）

3 个答案: