code: def G_synthesis(
      dlatents_in,  # Input: Disentangled latents (W) [minibatch, num_layers, dlatent_size].
      dlatent_size=512,  # Disentangled latent (W) dimensionality.
      num_channels=3,  # Number of output color channels.
      resolution=1024,  # Output resolution.
      fmap_base=8192,  # Overall multiplier for the number of feature maps.
      fmap_decay=1.0,  # log2 feature map reduction when doubling the resolution.
      fmap_max=512,  # Maximum number of feature maps in any layer.
      use_styles=True,  # Enable style inputs?
      const_input_layer=True,  # First layer is a learned constant?
      use_noise=True,  # Enable noise inputs?
      randomize_noise=True,  # True = randomize noise inputs every time (non-deterministic), False = read noise inputs from variables.
      nonlinearity="lrelu",  # Activation function: 'relu', 'lrelu'
      use_wscale=True,  # Enable equalized learning rate?
      use_pixel_norm=False,  # Enable pixelwise feature vector normalization?
      use_instance_norm=True,  # Enable instance normalization?
      dtype="float32",  # Data type to use for activations and outputs.
      fused_scale="auto",  # True = fused convolution + scaling, False = separate ops, 'auto' = decide automatically.
      blur_filter=[
          1,
          2,
          1,
      ],  # Low-pass filter to apply when resampling activations. None = no filtering.
      structure="auto",  # 'fixed' = no progressive growing, 'linear' = human-readable, 'recursive' = efficient, 'auto' = select automatically.
      is_template_graph=False,  # True = template graph constructed by the Network class, False = actual evaluation.
      force_clean_graph=False,  # True = construct a clean graph that looks nice in TensorBoard, False = default behavior.
      **_kwargs,
  ):  # Ignore unrecognized keyword args.
      resolution_log2 = int(np.log2(resolution))
      assert resolution == 2**resolution_log2 and resolution >= 4
  
      def nf(stage):
          return min(
              int(fmap_base / (2.0 ** (stage * fmap_decay))), fmap_max
          )
  
      def blur(x):
          return blur2d(x, blur_filter) if blur_filter else x
  
      if is_template_graph:
          force_clean_graph = True
      if force_clean_graph:
          randomize_noise = False
      if structure == "auto":
          structure = "linear" if force_clean_graph else "recursive"
      act, gain = {
          "relu": (tf.nn.relu, np.sqrt(2)),
          "lrelu": (leaky_relu, np.sqrt(2)),
      }[nonlinearity]
      num_layers = resolution_log2 * 2 - 2
      num_styles = num_layers if use_styles else 1
      images_out = None
  
      # Primary inputs.
      dlatents_in.set_shape([None, num_styles, dlatent_size])
      dlatents_in = tf.cast(dlatents_in, dtype)
      lod_in = tf.cast(
          tf.get_variable(
              "lod", initializer=np.float32(0), trainable=False
          ),
          dtype,
      )
  
      # Noise inputs.
      noise_inputs = []
      if use_noise:
          for layer_idx in range(num_layers):
              res = layer_idx // 2 + 2
              shape = [1, use_noise, 2**res, 2**res]
              noise_inputs.append(
                  tf.get_variable(
                      "noise%d" % layer_idx,
                      shape=shape,
                      initializer=tf.initializers.random_normal(),
                      trainable=False,
                  )
              )
  
      # Things to do at the end of each layer.
      def layer_epilogue(x, layer_idx):
          if use_noise:
              x = apply_noise(
                  x,
                  noise_inputs[layer_idx],
                  randomize_noise=randomize_noise,
              )
          x = apply_bias(x)
          x = act(x)
          if use_pixel_norm:
              x = pixel_norm(x)
          if use_instance_norm:
              x = instance_norm(x)
          if use_styles:
              x = style_mod(
                  x, dlatents_in[:, layer_idx], use_wscale=use_wscale
              )
          return x
  
      # Early layers.
      with tf.variable_scope("4x4"):
          if const_input_layer:
              with tf.variable_scope("Const"):
                  x = tf.get_variable(
                      "const",
                      shape=[1, nf(1), 4, 4],
                      initializer=tf.initializers.ones(),
                  )
                  x = layer_epilogue(
                      tf.tile(
                          tf.cast(x, dtype),
                          [tf.shape(dlatents_in)[0], 1, 1, 1],
                      ),
                      0,
                  )
          else:
              with tf.variable_scope("Dense"):
                  x = dense(
                      dlatents_in[:, 0],
                      fmaps=nf(1) * 16,
                      gain=gain / 4,
                      use_wscale=use_wscale,
                  )  # tweak gain to match the official implementation of Progressing GAN
                  x = layer_epilogue(
                      tf.reshape(x, [-1, nf(1), 4, 4]), 0
                  )
          with tf.variable_scope("Conv"):
              x = layer_epilogue(
                  conv2d(
                      x,
                      fmaps=nf(1),
                      kernel=3,
                      gain=gain,
                      use_wscale=use_wscale,
                  ),
                  1,
              )
  
      # Building blocks for remaining layers.
      def block(res, x):  # res = 3..resolution_log2
          with tf.variable_scope("%dx%d" % (2**res, 2**res)):
              with tf.variable_scope("Conv0_up"):
                  x = layer_epilogue(
                      blur(
                          upscale2d_conv2d(
                              x,
                              fmaps=nf(res - 1),
                              kernel=3,
                              gain=gain,
                              use_wscale=use_wscale,
                              fused_scale=fused_scale,
                          )
                      ),
                      res * 2 - 4,
                  )
              with tf.variable_scope("Conv1"):
                  x = layer_epilogue(
                      conv2d(
                          x,
                          fmaps=nf(res - 1),
                          kernel=3,
                          gain=gain,
                          use_wscale=use_wscale,
                      ),
                      res * 2 - 3,
                  )
              return x
  
      def torgb(res, x):  # res = 2..resolution_log2
          lod = resolution_log2 - res
          with tf.variable_scope("ToRGB_lod%d" % lod):
              return apply_bias(
                  conv2d(
                      x,
                      fmaps=num_channels,
                      kernel=1,
                      gain=1,
                      use_wscale=use_wscale,
                  )
              )
  
      # Fixed structure: simple and efficient, but does not support progressive growing.
      if structure == "fixed":
          for res in range(3, resolution_log2 + 1):
              x = block(res, x)
          images_out = torgb(resolution_log2, x)
  
      # Linear structure: simple but inefficient.
      if structure == "linear":
          images_out = torgb(2, x)
          for res in range(3, resolution_log2 + 1):
              lod = resolution_log2 - res
              x = block(res, x)
              img = torgb(res, x)
              images_out = upscale2d(images_out)
              with tf.variable_scope("Grow_lod%d" % lod):
                  images_out = tflib.lerp_clip(
                      img, images_out, lod_in - lod
                  )
  
      # Recursive structure: complex but efficient.
      if structure == "recursive":
  
          def cset(cur_lambda, new_cond, new_lambda):
              return lambda: tf.cond(new_cond, new_lambda, cur_lambda)
  
          def grow(x, res, lod):
              y = block(res, x)
              img = lambda: upscale2d(torgb(res, y), 2**lod)
              img = cset(
                  img,
                  (lod_in > lod),
                  lambda: upscale2d(
                      tflib.lerp(
                          torgb(res, y),
                          upscale2d(torgb(res - 1, x)),
                          lod_in - lod,
                      ),
                      2**lod,
                  ),
              )
              if lod > 0:
                  img = cset(
                      img,
                      (lod_in < lod),
                      lambda: grow(y, res + 1, lod - 1),
                  )
              return img()
  
          images_out = grow(x, 3, resolution_log2 - 3)
  
      assert images_out.dtype == tf.as_dtype(dtype)
      return tf.identity(images_out, name="images_out")
  
imports: import numpy
  import tensorflow
  import dnnlib
  import dnnlib.tflib
label_desc: Use image node_id 21 for calling a global function with example usage: G_synthesis(dlatents_in, dlatent_size, num_channels, resolution, fmap_base, fmap_decay, fmap_max, use_styles, const_input_layer, use_noise, randomize_noise, nonlinearity, use_wscale, use_pixel_norm, use_instance_norm, dtype, fused_scale, blur_filter, structure, is_template_graph, force_clean_graph) and returns: tf, min, unknown, x, x, apply_bias, function, img