Where are the type and weight of the activation function in .tflite?

Question

I'm doing Post Training Aware, and want to get type and weight of the activation of Conv1D, like 'relu'. So I use tf.lite.experimental.Analyzer.analyze(model_content=tflite_model_quant) to check, but I don't see anything about activation. How can I get it?

Analyzer Output

T#18(sequential_3/quant_conv1d_13/Conv1D) shape:[16, 1, 16, 4], type:INT8 RO 1024 bytes, buffer: 19, data:[., ., ., ., %, ...]
  T#19(sequential_3/quant_conv1d_12/BiasAdd/ReadVariableOp) shape:[4], type:INT32 RO 16 bytes, buffer: 20, data:[-220, 862, 1177, -1029]
  T#20(sequential_3/quant_conv1d_12/Conv1D) shape:[4, 1, 16, 1], type:INT8 RO 64 bytes, buffer: 21, data:[., :, ., ., ., ...]
  T#21(sequential_3/quant_conv1d_12/Conv1D/ExpandDims) shape_signature:[-1, 1, 256, 1], type:INT8
  T#22(sequential_3/quant_conv1d_12/Relu;sequential_3/quant_conv1d_12/BiasAdd;sequential_3/quant_conv1d_12/Conv1D/Squeeze;sequential_3/quant_conv1d_12/BiasAdd/ReadVariableOp;sequential_3/quant_conv1d_12/Conv1D) shape_signature:[-1, 1, 256, 4], type:INT8
  T#23(sequential_3/quant_conv1d_12/Relu;sequential_3/quant_conv1d_12/BiasAdd;sequential_3/quant_conv1d_12/Conv1D/Squeeze;sequential_3/quant_conv1d_12/BiasAdd/ReadVariableOp) shape_signature:[-1, 256, 4], type:INT8

My Model

def build_model():
  model = tf.keras.models.Sequential([
  tf.keras.layers.Conv1D(4, 16, strides= 1,padding='same', activation= 'relu'),
  tf.keras.layers.MaxPooling1D(pool_size=3, strides=2, padding='same'),
  tf.keras.layers.Conv1D(16, 16, strides=1, padding='same', activation='relu'),
  tf.keras.layers.MaxPooling1D(pool_size=3, strides=2, padding='same'),
  tf.keras.layers.Conv1D(32, 16, strides=1, padding='same', activation='relu'),
  tf.keras.layers.AveragePooling1D(pool_size=3, strides=2, padding='same'),
  tf.keras.layers.Conv1D(64, 16, strides=1, padding= 'same', activation='relu'),
  tf.keras.layers.MaxPooling1D(pool_size=3, strides=2, padding='same'),
  tf.keras.layers.Flatten(),
  tf.keras.layers.Dense(128, activation='relu'),
  #tf.keras.layers.Dropout(0.5),
  tf.keras.layers.Dense(15, activation='softmax')
  ])
  model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['acc'])
  return model