class_docstr: Implement a residual block.
class_docstr_tok: ["Implement","a","residual","block","."]
docstr: Instantiate layer.
  
  Args:
    filters: int, number of filters, passed to the conv layers.
    kernel_size: int, kernel_size, passed to the conv layers.
    name: str, name of the layer.
    activation: function or string, resolved with keras.
    **kwargs_conv2d: Additional arguments to be passed directly to Conv2D.
      E.g. 'padding'.
docstr_tok: ["Instantiate","layer",".","Args",":","filters",":","int",",","number","of","filters",",","passed","to","the","conv","layers",".","kernel_size",":","int",",","kernel_size",",","passed","to","the","conv","layers",".","name",":","str",",","name","of","the","layer",".","activation",":","function","or","string",",","resolved","with","keras",".","*","*","kwargs_conv2d",":","Additional","arguments","to","be","passed","directly","to","Conv2D",".","E.g",".","'padding","'","."]
code: def __init__(
      self,
      filters,
      kernel_size,
      name=None,
      activation="relu",
      **kwargs_conv2d,
  ):
      """Instantiate layer.
  
      Args:
        filters: int, number of filters, passed to the conv layers.
        kernel_size: int, kernel_size, passed to the conv layers.
        name: str, name of the layer.
        activation: function or string, resolved with keras.
        **kwargs_conv2d: Additional arguments to be passed directly to Conv2D.
          E.g. 'padding'.
      """
      super(ResidualBlock, self).__init__()
  
      kwargs_conv2d["filters"] = filters
      kwargs_conv2d["kernel_size"] = kernel_size
  
      block = [
          tf.keras.layers.Conv2D(**kwargs_conv2d),
          ChannelNorm(),
          tf.keras.layers.Activation(activation),
          tf.keras.layers.Conv2D(**kwargs_conv2d),
          ChannelNorm(),
      ]
  
      self.block = tf.keras.Sequential(name=name, layers=block)
  
imports: import collections
  from compare_gan.architectures import abstract_arch
  from compare_gan.architectures import arch_ops
  import numpy
  import tensorflow.compat.v1
  import tensorflow_compression
  from hific.helpers import ModelMode
label_desc: Use image node_id 1 to create a new ResidualBlock object from inherited base classes: tf.keras.layers with example: obj = ResidualBlock(filters, kernel_size, name, activation)