input: Write a python function to set up a TensorFlow model with placeholder inputs and train it.
  assert isinstance(train_model(tf.placeholder(tf.float32, shape=[None, 3]), tf.placeholder(tf.float32, shape=[None, 2])), tf.keras.Sequential)
  assert train_model(tf.placeholder(tf.float32, shape=[None, 3]), tf.placeholder(tf.float32, shape=[None, 2])).count_params() > 0
output: import tensorflow as tf
  
  def train_model(input_placeholder, output_placeholder):
      model = tf.keras.Sequential([
          tf.keras.layers.Dense(5, activation='relu', input_shape=(3,)),
          tf.keras.layers.Dense(2, activation='softmax')
      ])
      
      model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
      
      # Dummy data for example purposes
      import numpy as np
      x_train = np.random.rand(100, 3)
      y_train = np.random.rand(100, 2)
      
      model.fit(x_train, y_train, epochs=1, batch_size=10)
      
      return model