class_docstr: Implementation of the Residual in Residual Dense Network for image super-scaling.
  
  The network is the one described in https://arxiv.org/abs/1809.00219 (Wang et al. 2018).
  
  Args:
      arch_params: dictionary, contains the network parameters C, D, G, G0, T, x.
      patch_size: integer or None, determines the input size. Only needed at
          training time, for prediction is set to None.
      beta: float <= 1, scaling parameter for the residual connections.
      c_dim: integer, number of channels of the input image.
      kernel_size: integer, common kernel size for convolutions.
      upscaling: string, 'ups' or 'shuffle', determines which implementation
          of the upscaling layer to use.
      init_val: extreme values for the RandomUniform initializer.
      weights: string, if not empty, download and load pre-trained weights.
          Overrides other parameters.
  
  Attributes:
      C: integer, number of conv layer inside each residual dense blocks (RDB).
      D: integer, number of RDBs inside each Residual in Residual Dense Block (RRDB).
      T: integer, number or RRDBs.
      G: integer, number of convolution output filters inside the RDBs.
      G0: integer, number of output filters of each RDB.
      x: integer, the scaling factor.
      model: Keras model of the RRDN.
      name: name used to identify what upscaling network is used during training.
      model._name: identifies this network as the generator network
          in the compound model built by the trainer class.
code: def _build_rdn(self):
      LR_input = Input(
          shape=(self.patch_size, self.patch_size, 3), name="LR_input"
      )
      pre_blocks = Conv2D(
          self.G0,
          kernel_size=self.kernel_size,
          padding="same",
          kernel_initializer=self.initializer,
          name="Pre_blocks_conv",
      )(LR_input)
      # DIFFERENCE: in RDN an extra convolution is present here
      for t in range(1, self.T + 1):
          if t == 1:
              x = self._RRDB(pre_blocks, t)
          else:
              x = self._RRDB(x, t)
      # DIFFERENCE: in RDN a conv with kernel size of 1 after a concat operation is used here
      post_blocks = Conv2D(
          self.G0,
          kernel_size=3,
          padding="same",
          kernel_initializer=self.initializer,
          name="post_blocks_conv",
      )(x)
      # Global Residual Learning
      GRL = Add(name="GRL")([post_blocks, pre_blocks])
      # Upscaling
      PS = self._pixel_shuffle(GRL)
      # Compose SR image
      SR = Conv2D(
          self.c_dim,
          kernel_size=self.kernel_size,
          padding="same",
          kernel_initializer=self.initializer,
          name="SR",
      )(PS)
      return Model(inputs=LR_input, outputs=SR)
  
imports: import tensorflow
  from tensorflow.keras.initializers import RandomUniform
  from tensorflow.keras.layers import concatenate, Input, Activation, Add, Conv2D, Lambda
  from tensorflow.keras.models import Model
  from ISR.models.imagemodel import ImageModel
label_desc: Use image node_id 5 for calling the RRDN obj's underlying member method code with example usage: obj._build_rdn() and returns: Model