title: What does hidden_layer = layers.Dense(100, activation="relu")(input_layer) do?
body: <p>I saw the following in <a href="https://www.analyticsvidhya.com/blog/2018/04/a-comprehensive-guide-to-understand-and-implement-text-classification-in-python/" rel="nofollow noreferrer">a guide to text classification</a>:</p>
  <pre><code>hidden_layer = layers.Dense(100, activation=&quot;relu&quot;)(input_layer)
  </code></pre>
  <p>What does the <code>(input_layer)</code> mean?</p>
  <p>The context is:</p>
  <pre><code>def create_model_architecture(input_size):
  
      # create input layer 
      input_layer = layers.Input((input_size, ), sparse=True)
      
      # create hidden layer
      hidden_layer = layers.Dense(100, activation=&quot;relu&quot;)(input_layer)
      
      # create output layer
      output_layer = layers.Dense(1, activation=&quot;sigmoid&quot;)(hidden_layer)
  
      classifier = models.Model(inputs = input_layer, outputs = output_layer)
      classifier.compile(optimizer=optimizers.Adam(), loss='binary_crossentropy')
      return classifier 
  </code></pre>
  <p>Also - is this a sequential model?</p>