input: Write a function to read a CSV file, split the data into training and testing sets, train a RandomForestClassifier model, and evaluate its accuracy.
  ```python
  def process_and_train_model(file_path):
      import pandas as pd 
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestClassifier
  
      # Read the data
      data = pd.read_csv(file_path)
  
      # Extract the features and labels from the data
      X = data.drop('Survived', axis=1)
      y = data['Survived']
  
      # 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)
  
      # Initialize the Random Forest model
      clf = RandomForestClassifier(n_estimators=1000, max_depth=10, random_state=0)
  
      # Train the model
      clf.fit(X_train, y_train)
  
      # Evaluate the model
      accuracy = clf.score(X_test, y_test)
      return accuracy
  
  assert process_and_train_model('input1.csv') >= 0  # Model accuracy should be at least 0
  assert process_and_train_model('input2.csv') <= 1  # Model accuracy should be at most 1
  assert isinstance(process_and_train_model('input1.csv'), float)  # Model accuracy should be a float
output: def process_and_train_model(file_path):
      import pandas as pd 
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestClassifier
  
      # Read the data
      data = pd.read_csv(file_path)
  
      # Extract the features and labels from the data
      X = data.drop('Survived', axis=1)
      y = data['Survived']
  
      # 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)
  
      # Initialize the Random Forest model
      clf = RandomForestClassifier(n_estimators=1000, max_depth=10, random_state=0)
  
      # Train the model
      clf.fit(X_train, y_train)
  
      # Evaluate the model
      accuracy = clf.score(X_test, y_test)
      return accuracy