class_docstr: Layer expects image as an input with second dimension
  sepcified as a channel. Image will be sliced over the channel.
  The ``from`` and ``to`` indices can be specified as the parameters.
  
  Parameters
  ----------
  from_channel : int
      From which channel we will start slicing.
  
  to_channel : int
      To which channel we will be slicing. This layer won't be
      included in the output.
  
  {BaseLayer.name}
code: def get_output_shape(self, input_shape):
      n_channels = self.to_channel - self.from_channel
  
      if input_shape.ndims is None:
          return tf.TensorShape((None, None, None, n_channels))
  
      if input_shape.ndims != 4:
          raise ValueError(
              "Layer {} expects 4 dimensional inputs, got {} instead."
              "".format(self.name, input_shape.ndims)
          )
  
      n_samples, height, width, _ = input_shape
      return tf.TensorShape((n_samples, height, width, n_channels))
  
file_path: neupy/examples/cnn/alexnet.py
imports: import tensorflow
  from neupy.layers import *
label_desc: Use image node_id 2 for calling the SliceChannels obj's underlying member method code with example usage: obj.get_output_shape(input_shape) and returns: tf, tf