code: def MLP(
      layer_widths=(128, 64),
      activation_fn=tl.Relu,
      out_activation=False,
      flatten=True,
      mode="train",
  ):
      """A "multilayer perceptron" (MLP) network.
  
      This is a classic fully connected feedforward network, with one or more
      layers and a (nonlinear) activation function between each layer. For
      historical reasons, such networks are often called multilayer perceptrons;
      but they are more accurately described as multilayer networks, where
      each layer + activation function is a perceptron-like unit (see, e.g.,
      [https://en.wikipedia.org/wiki/Multilayer_perceptron#Terminology]).
  
      Args:
        layer_widths: Tuple of ints telling the number of layers and the width of
            each layer. For example, setting `layer_widths=(128, 64, 32)` would
            yield 3 layers with successive widths of 128, 64, and 32.
        activation_fn: Type of activation function between pairs of fully connected
            layers; must be an activation-type subclass of `Layer`.
        out_activation: If True, include a copy of the activation function as the
            last layer in the network.
        flatten: If True, insert a layer at the head of the network to flatten the
            input tensor into a matrix of shape (batch_size. -1).
        mode: Ignored.
  
      Returns:
        An assembled MLP network with the specified layers. This network can either
        be initialized and trained as a full model, or can be used as a building
        block in a larger network.
      """
      del mode
  
      layers = []
      for width in layer_widths:
          layers.append(tl.Dense(width))
          layers.append(activation_fn())
  
      if not out_activation:
          # Don't need the last activation.
          layers.pop()
  
      return tl.Serial(
          [tl.Flatten()] if flatten else [],
          layers,
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: MLP(layer_widths, activation_fn, out_activation, flatten, mode) and returns: tl