input: Write a python function to create and compile a TensorFlow model with two input features, a hidden layer with three neurons using sigmoid activation, and an output layer with three neurons using softmax activation.
  assert isinstance(create_compile_model(), tf.keras.Model)
  assert create_compile_model().layers[1].units == 3
  assert create_compile_model().layers[2].activation.__name__ == 'softmax'
output: def create_compile_model():
      import tensorflow as tf
      from tensorflow.keras.models import Model
      from tensorflow.keras.layers import Input, Dense
      
      # Define input layer
      inputs = Input(shape=(2,))
      
      # Define hidden layer with 3 neurons and sigmoid activation
      hidden_layer = Dense(3, activation='sigmoid')(inputs)
      
      # Define output layer with 3 neurons and softmax activation
      outputs = Dense(3, activation='softmax')(hidden_layer)
      
      # Create the model
      model = Model(inputs=inputs, outputs=outputs)
      
      # Compile the model
      model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
      
      return model