input: Write a python function to perform forward propagation in a neural network using TensorFlow.
  assert forwardPropagation(tf.placeholder(tf.float32, [None, 5])) is not None
  assert isinstance(forwardPropagation(tf.placeholder(tf.float32, [None, 5])), tf.Tensor)
  assert forwardPropagation(tf.placeholder(tf.float32, [None, 5])).shape[-1] == 1
output: import tensorflow as tf
  
  def forwardPropagation(inputs):
      # Define weights and biases
      weights = tf.Variable(tf.random.normal([5, 1]), dtype=tf.float32)
      biases = tf.Variable(tf.random.normal([1]), dtype=tf.float32)
  
      # Forward propagation: compute the linear combination and apply activation function
      output = tf.add(tf.matmul(inputs, weights), biases)
      
      return output