input: Write a python function to implement a random forest classifier using TensorFlow.
  assert isinstance(implement_random_forest_classifier(X_train, y_train, X_test), float)
  assert implement_random_forest_classifier(X_train, y_train, X_test) > 0.5
output: def implement_random_forest_classifier(X_train, y_train, X_test):
      import tensorflow as tf
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.metrics import accuracy_score
  
      # Train Random Forest Classifier
      rf = RandomForestClassifier(n_estimators=100)
      rf.fit(X_train, y_train)
      
      # Predict on test data
      y_pred = rf.predict(X_test)
      
      # Calculate accuracy
      accuracy = accuracy_score(y_test, y_pred)
      
      return accuracy