input: Write a python function to train and evaluate a K-Nearest Neighbors classifier on the Iris dataset.
  assert isinstance(train_and_evaluate_knn('iris.csv')[0], np.ndarray)
  assert isinstance(train_and_evaluate_knn('iris.csv')[1], np.ndarray)
  assert train_and_evaluate_knn('iris.csv')[0].shape == (30, 4)
  assert train_and_evaluate_knn('iris.csv')[1].shape == (30, 30)
output: def train_and_evaluate_knn(file_path):
      import numpy as np
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import StandardScaler
      from sklearn.neighbors import KNeighborsClassifier
      from sklearn.metrics import pairwise_distances
  
      # Load the dataset
      df = pd.read_csv(file_path)
  
      # Split the data into features and target
      X = df.drop(columns=['species'])
      y = df['species']
  
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Standardize the features
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      # Train the K-Nearest Neighbors classifier
      knn = KNeighborsClassifier(n_neighbors=5)
      knn.fit(X_train, y_train)
  
      # Evaluate the classifier
      y_pred = knn.predict(X_test)
  
      # Compute the distances between the test samples
      distances = pairwise_distances(X_test, metric='euclidean')
  
      return X_test, distances