class_docstr: Upscales input over two axis (height and width).
  
  Parameters
  ----------
  scale : int or tuple with two int
      Scaling factor for the input value. In the tuple first
      parameter identifies scale of the height and the second
      one of the width.
  
  {BaseLayer.name}
  
  Methods
  -------
  {BaseLayer.Methods}
  
  Attributes
  ----------
  {BaseLayer.Attributes}
  
  Examples
  --------
  >>> from neupy.layers import *
  >>> network = Input((10, 10, 3)) >> Upscale((2, 2))
  (?, 10, 10, 3) -> [... 2 layers ...] -> (?, 20, 20, 3)
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code: def __init__(self, scale, name=None):
      super(Upscale, self).__init__(name=name)
  
      if isinstance(scale, int):
          scale = as_tuple(scale, scale)
  
      if any(element <= 0 for element in scale):
          raise ValueError(
              "Only positive integers are allowed for scale"
          )
  
      self.scale = scale
  
imports: from __future__ import division
  import math
  import tensorflow
  from neupy.utils import as_tuple, tf_utils
  from neupy.core.properties import TypedListProperty, ChoiceProperty, FunctionWithOptionsProperty
  from neupy.exceptions import LayerConnectionError
  from .base import BaseLayer
  from .convolutions import Spatial2DProperty
label_desc: Use image node_id 1 to create a new Upscale object from inherited base classes: BaseLayer with example: obj = Upscale(scale, name)