Is the activation function for each layer stored in the .h5 file produced by model.save()? Or is it already "baked in" to the weights?

I am writing an AWS Lambda function to generate time-series predictions from multiple regression models every five minutes. Unfortunately, TensorFlow is too large of a library to be loaded into an AWS Lambda function, so I am writing my own Python code to load the saved .h5 model file and generate predictions based on the weights and input data. Here's where I'm at so far:

```
def generate_predictions(model_path, df):
    model_info = h5py.File(model_path, 'r')
    model_weights = model_info['model_weights']
    # Initialize predictions matrix with preprocessed inputs
    predictions = preprocessing.scale(df[inputs])
    layer_list = list(model_weights.keys())
    for layer in layer_list:
        weights = model_weights[layer][layer]['kernel:0'][:]
        bias = model_weights[layer][layer]['bias:0'][:]
        predictions = predictions.dot(weights)
        predictions += bias
        # How to retrieve activation function for layer?
        # predictions = activation_function(predictions)

    return predictions

```

I understand I'll probably want some kind of case/switch statement to handle the various activation functions.