code: def __init__(
      self,
      out_channels,
      kernel_size,
      strides,
      padding,
      drop_r,
      n_layers,
      layer_type="down",
  ):
      super(Block, self).__init__()
      self.layer_list = []
      self.layer_list2 = []
      self.n_layers = n_layers
      for _ in range(n_layers):
          self.layer_list.append(
              Conv3D(
                  out_channels,
                  kernel_size,
                  strides=strides,
                  padding=padding,
              )
          )
          self.layer_list.append(Dropout(drop_r))
          self.layer_list.append(LayerNormalization())
          self.layer_list.append(ReLU())
      if layer_type == "down":
          self.layer_list2.append(
              Conv3D(1, 2, strides=2, padding="same")
          )
          self.layer_list2.append(ReLU())
      elif layer_type == "up":
          self.layer_list2.append(
              Conv3DTranspose(1, 2, strides=2, padding="same")
          )
          self.layer_list2.append(ReLU())
  
      self.channel_sum = ChannelSum()
      self.add = Add()
  
imports: import logging
  import numpy
  from dipy.data import get_fnames
  from dipy.testing.decorators import doctest_skip_parser
  from dipy.utils.optpkg import optional_package
  from dipy.align.reslice import reslice
  from dipy.nn.utils import normalize, set_logger_level, transform_img
  from dipy.nn.utils import recover_img, correct_minor_errors
label_desc: Use image node_id 1 to create a new Block object from inherited base classes: Layer with example: obj = Block(out_channels, kernel_size, strides, padding, drop_r, n_layers, layer_type)