code: def generator(
      z,
      progress,
      num_filters_fn,
      resolution_schedule,
      num_blocks=None,
      kernel_size=3,
      colors=3,
      to_rgb_activation=None,
      simple_arch=False,
      scope="progressive_gan_generator",
      reuse=None,
  ):
      """Generator network for the progressive GAN model.
  
      Args:
        z: A `Tensor` of latent vector. The first dimension must be batch size.
        progress: A scalar float `Tensor` of training progress.
        num_filters_fn: A function that maps `block_id` to # of filters for the
            block.
        resolution_schedule: An object of `ResolutionSchedule`.
        num_blocks: An integer of number of blocks. None means maximum number of
            blocks, i.e. `resolution.schedule.num_resolutions`. Defaults to None.
        kernel_size: An integer of convolution kernel size.
        colors: Number of output color channels. Defaults to 3.
        to_rgb_activation: Activation function applied when output rgb.
        simple_arch: Architecture variants for lower memory usage and faster speed
        scope: A string or variable scope.
        reuse: Whether to reuse `scope`. Defaults to None which means to inherit
            the reuse option of the parent scope.
      Returns:
        A `Tensor` of model output and a dictionary of model end points.
      """
      if num_blocks is None:
          num_blocks = resolution_schedule.num_resolutions
  
      start_h, start_w = resolution_schedule.start_resolutions
      final_h, final_w = resolution_schedule.final_resolutions
  
      def _conv2d(scope, x, kernel_size, filters, padding="SAME"):
          return layers.custom_conv2d(
              x=x,
              filters=filters,
              kernel_size=kernel_size,
              padding=padding,
              activation=lambda x: layers.pixel_norm(
                  tf.nn.leaky_relu(x)
              ),
              he_initializer_slope=0.0,
              scope=scope,
          )
  
      def _to_rgb(x):
          return layers.custom_conv2d(
              x=x,
              filters=colors,
              kernel_size=1,
              padding="SAME",
              activation=to_rgb_activation,
              scope="to_rgb",
          )
  
      he_init = tf_slim.variance_scaling_initializer()
  
      end_points = {}
  
      with tf.variable_scope(scope, reuse=reuse):
          with tf.name_scope("input"):
              x = tf_slim.flatten(z)
              end_points["latent_vector"] = x
  
          with tf.variable_scope(block_name(1)):
              if simple_arch:
                  x_shape = tf.shape(x)
                  x = tf.layers.dense(
                      x,
                      start_h * start_w * num_filters_fn(1),
                      kernel_initializer=he_init,
                  )
                  x = tf.nn.relu(x)
                  x = tf.reshape(
                      x,
                      [x_shape[0], start_h, start_w, num_filters_fn(1)],
                  )
              else:
                  x = tf.expand_dims(tf.expand_dims(x, 1), 1)
                  x = layers.pixel_norm(x)
                  # Pad the 1 x 1 image to 2 * (start_h - 1) x 2 * (start_w - 1)
                  # with zeros for the next conv.
                  x = tf.pad(
                      x,
                      [
                          [0] * 2,
                          [start_h - 1] * 2,
                          [start_w - 1] * 2,
                          [0] * 2,
                      ],
                  )
                  # The output is start_h x start_w x num_filters_fn(1).
                  x = _conv2d(
                      "conv0",
                      x,
                      (start_h, start_w),
                      num_filters_fn(1),
                      "VALID",
                  )
                  x = _conv2d(
                      "conv1", x, kernel_size, num_filters_fn(1)
                  )
              lods = [x]
  
          if resolution_schedule.scale_mode == "H":
              strides = (resolution_schedule.scale_base, 1)
          else:
              strides = (
                  resolution_schedule.scale_base,
                  resolution_schedule.scale_base,
              )
  
          for block_id in range(2, num_blocks + 1):
              with tf.variable_scope(block_name(block_id)):
                  if simple_arch:
                      x = tf.layers.conv2d_transpose(
                          x,
                          num_filters_fn(block_id),
                          kernel_size=kernel_size,
                          strides=strides,
                          padding="SAME",
                          kernel_initializer=he_init,
                      )
                      x = tf.nn.relu(x)
                  else:
                      x = resolution_schedule.upscale(
                          x, resolution_schedule.scale_base
                      )
                      x = _conv2d(
                          "conv0",
                          x,
                          kernel_size,
                          num_filters_fn(block_id),
                      )
                      x = _conv2d(
                          "conv1",
                          x,
                          kernel_size,
                          num_filters_fn(block_id),
                      )
                  lods.append(x)
  
          outputs = []
          for block_id in range(1, num_blocks + 1):
              with tf.variable_scope(block_name(block_id)):
                  if simple_arch:
                      lod = lods[block_id - 1]
                      lod = tf.layers.conv2d(
                          lod,
                          colors,
                          kernel_size=1,
                          padding="SAME",
                          name="to_rgb",
                          kernel_initializer=he_init,
                      )
                      lod = to_rgb_activation(lod)
                  else:
                      lod = _to_rgb(lods[block_id - 1])
                  scale = resolution_schedule.scale_factor(block_id)
                  lod = resolution_schedule.upscale(lod, scale)
                  end_points["upscaled_rgb_{}".format(block_id)] = lod
  
                  # alpha_i is used to replace lod_select. Note sum(alpha_i) is
                  # garanteed to be 1.
                  alpha = _generator_alpha(block_id, progress)
                  end_points["alpha_{}".format(block_id)] = alpha
  
                  outputs.append(lod * alpha)
  
          predictions = tf.add_n(outputs)
          batch_size = int(z.shape[0])
          predictions.set_shape([batch_size, final_h, final_w, colors])
          end_points["predictions"] = predictions
  
      return predictions, end_points
  
imports: import math
  from magenta.models.gansynth.lib import layers
  import tensorflow.compat.v1
  import tf_slim
label_desc: Use image node_id 9 for calling a global function with example usage: generator(z, progress, num_filters_fn, resolution_schedule, num_blocks, kernel_size, colors, to_rgb_activation, simple_arch, scope, reuse) and returns: predictions,  end_points, layers, layers