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.
docstr: Residual in Residual Dense Block.
  
  t is integer, for naming of RRDB.
  beta is scalar.
docstr_tok: ["Residual","in","Residual","Dense","Block",".","t","is","integer",",","for","naming","of","RRDB",".","beta","is","scalar","."]
code: def _RRDB(self, input_layer, t):
      """Residual in Residual Dense Block.
  
      t is integer, for naming of RRDB.
      beta is scalar.
      """
  
      # SUGGESTION: MAKE BETA LEARNABLE
      x = input_layer
      for d in range(1, self.D + 1):
          LFF = self._dense_block(x, d, t)
          LFF_beta = MultiplyBeta(self.beta)(LFF)
          x = Add(name="LRL_%d_%d" % (t, d))([x, LFF_beta])
      x = MultiplyBeta(self.beta)(x)
      x = Add(name="RRDB_%d_out" % (t))([input_layer, x])
      return x
  
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 3 for calling the RRDN obj's underlying member method code with example usage: obj._RRDB(input_layer, t) and returns: x