class_docstr: This class defines a high-resolution block with residual connections
  kernels
  
      - specify kernel sizes of each convolutional layer
      - e.g.: kernels=(5, 5, 5) indicate three conv layers of kernel_size 5
  
  with_res
  
      - whether to add residual connections to bypass the conv layers
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docstr: :param n_output_chns: int, number of output channels
  :param kernels: list of layer kernel sizes
  :param acti_func: activation function to use
  :param w_initializer: weight initialisation for network
  :param w_regularizer: weight regularisation for network
  :param with_res: boolean, set to True if residual connection are to use
  :param name: layer name
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code: def __init__(
      self,
      n_output_chns,
      kernels=(3, 3),
      acti_func="relu",
      w_initializer=None,
      w_regularizer=None,
      with_res=True,
      name="HighResBlock",
  ):
      """
  
      :param n_output_chns: int, number of output channels
      :param kernels: list of layer kernel sizes
      :param acti_func: activation function to use
      :param w_initializer: weight initialisation for network
      :param w_regularizer: weight regularisation for network
      :param with_res: boolean, set to True if residual connection are to use
      :param name: layer name
      """
  
      super(HighResBlock, self).__init__(name=name)
  
      self.n_output_chns = n_output_chns
      if hasattr(kernels, "__iter__"):  # a list of layer kernel_sizes
          self.kernels = kernels
      else:  # is a single number (indicating single layer)
          self.kernels = [kernels]
      self.acti_func = acti_func
      self.with_res = with_res
  
      self.initializers = {"w": w_initializer}
      self.regularizers = {"w": w_regularizer}
  
file_path: NiftyNet/niftynet/network/highres3dnet.py
imports: from __future__ import absolute_import, print_function
  from six.moves import range
  from niftynet.layer import layer_util
  from niftynet.layer.activation import ActiLayer
  from niftynet.layer.base_layer import TrainableLayer
  from niftynet.layer.bn import BNLayer
  from niftynet.layer.convolution import ConvLayer, ConvolutionalLayer
  from niftynet.layer.dilatedcontext import DilatedTensor
  from niftynet.layer.elementwise import ElementwiseLayer
  from niftynet.network.base_net import BaseNet
label_desc: Use image node_id 1 to create a new HighResBlock object from inherited base classes: TrainableLayer with example: obj = HighResBlock(n_output_chns, kernels, acti_func, w_initializer, w_regularizer, with_res, name)