class_docstr: A de-convolution 3D layer.
  
  See `tf.nn.conv3d_transpose <https://tensorflow.google.cn/versions/r2.0/api_docs/python/tf/nn/conv3d_transpose>`__.
  
  Parameters
  ----------
  act : activation function or None
      The activation function of this layer.
  shape : tuple of int
      The shape of the filters: (depth, height, width, output_channels, in_channels).
      The filter's in_channels dimension must match that of value.
  outputs_shape : tuple of int
      The output shape of the deconvolution.
  strides : tuple of int
      The sliding window strides for corresponding input dimensions.
  padding : str
      The padding algorithm type: "SAME" or "VALID".
  data_format : str
      "NDHWC" or "NCDHW", default is "NDHWC".
  dilation_rate : tuple of int
      Filter up-sampling/input down-sampling rate.
  W_init : initializer
      The initializer for the weight matrix.
  b_init : initializer or None
      The initializer for the bias vector. If None, skip biases.
  name : None or str
      A unique layer name.
  
  Notes
  -----
  - shape = [d, h, w, the number of output channels of this layer, the number of output channel of the previous layer].
  - outputs_shape = [batch_size, any, any, any, the number of output channels of this layer].
  - the number of output channel of a layer is its last dimension.
  
  Examples
  --------
  >>> input_layer = Input([8, 10, 10, 10 32], name='input_layer')
  >>> deconv3d = tl.layers.DeConv3dLayer(
  ...     shape=(2, 2, 2, 128, 32), outputs_shape=(8, 20, 20, 20, 128), strides=(1, 2, 2, 2, 1), name='deconv3dlayer'
  ... )
  >>> print(deconv3d)
  >>> tensor = tl.layers.DeConv1dLayer(
  ...     shape=(2, 2, 2, 128, 32), outputs_shape=(8, 20, 20, 20, 128), strides=(1, 2, 2, 2, 1), name='deconv3dlayer'
  ... )(input_layer)
  >>> print(tensor)
  >>> output shape : (8, 20, 20, 20, 128)
code: def __repr__(self):
      actstr = (
          self.act.__name__ if self.act is not None else "No Activation"
      )
      s = (
          "{classname}(in_channels={in_channels}, out_channels={n_filter}, kernel_size={filter_size}"
          ", strides={strides}, padding={padding}"
      )
      if self.dilation_rate != (1,) * len(self.dilation_rate):
          s += ", dilation={dilation_rate}"
      if self.b_init is None:
          s += ", bias=False"
      s += ", " + actstr
      if self.name is not None:
          s += ", name='{name}'"
      s += ")"
      return s.format(
          classname=self.__class__.__name__,
          n_filter=self.shape[-2],
          filter_size=(self.shape[0], self.shape[1], self.shape[2]),
          **self.__dict__,
      )
  
imports: import tensorflow
  import tensorlayer
  from tensorlayer import logging
  from tensorlayer.decorators import deprecated_alias
  from tensorlayer.layers.core import Layer
label_desc: Use image node_id 2 for calling the DeConv3dLayer obj's underlying member method code with example usage: obj.__repr__() and returns: s