class_docstr: Deconvolution layer (also known as Transposed Convolution.).
  
  Parameters
  ----------
  {Convolution.size}
  
  {Convolution.padding}
  
  {Convolution.stride}
  
  {Convolution.dilation}
  
  weight : array-like, Tensorfow variable, scalar or Initializer
      Defines layer's weights. Shape of the weight will be equal to
      ``(filter rows, filter columns, output channels, input channels)``.
      Default initialization methods you can find
      :ref:`here <init-methods>`. Defaults to
      :class:`HeNormal(gain=2) <neupy.init.HeNormal>`.
  
  {Convolution.bias}
  
  {Convolution.name}
  
  Methods
  -------
  {Convolution.Methods}
  
  Attributes
  ----------
  {Convolution.Attributes}
  
  Examples
  --------
  >>> from neupy.layers import *
  >>> network = join(
  ...     Input((28, 28, 3)),
  ...     Convolution((3, 3, 16)),
  ...     Deconvolution((3, 3, 1)),
  ... )
  >>> network
  (?, 28, 28, 3) -> [... 3 layers ...] -> (?, 28, 28, 1)
code: def create_variables(self, input_shape):
      self.input_shape = input_shape
      n_channels = input_shape[-1]
      n_rows, n_cols, n_filters = self.size
  
      # Compare to the regular convolution weights,
      # transposed one has switched input and output channels.
      self.weight = self.variable(
          value=self.weight,
          name="weight",
          shape=(n_rows, n_cols, n_filters, n_channels),
      )
  
      if self.bias is not None:
          self.bias = self.variable(
              value=self.bias, name="bias", shape=as_tuple(n_filters)
          )
  
imports: from __future__ import division
  import math
  import collections
  import six
  import tensorflow
  from neupy import init
  from neupy.utils import as_tuple
  from neupy.exceptions import LayerConnectionError
  from neupy.core.properties import TypedListProperty, Property, ParameterProperty
  from .base import BaseLayer
label_desc: Use image node_id 3 for calling the Deconvolution obj's underlying member method code with example usage: obj.create_variables(input_shape) without return types