class_docstr: Simplified version of :class:`Conv3dLayer`.
  
  Parameters
  ----------
  n_filter : int
      The number of filters.
  filter_size : tuple of int
      The filter size (height, width).
  strides : tuple of int
      The sliding window strides of corresponding input dimensions.
      It must be in the same order as the ``shape`` parameter.
  dilation_rate : tuple of int
      Specifying the dilation rate to use for dilated convolution.
  act : activation function
      The activation function of this layer.
  padding : str
      The padding algorithm type: "SAME" or "VALID".
  data_format : str
      "channels_last" (NDHWC, default) or "channels_first" (NCDHW).
  W_init : initializer
      The initializer for the the weight matrix.
  b_init : initializer or None
      The initializer for the the bias vector. If None, skip biases.
  in_channels : int
      The number of in channels.
  name : None or str
      A unique layer name.
  
  Examples
  --------
  With TensorLayer
  
  >>> net = tl.layers.Input([8, 20, 20, 20, 3], name='input')
  >>> conv3d = tl.layers.Conv2d(n_filter=32, filter_size=(3, 3, 3), strides=(2, 2, 2), b_init=None, in_channels=3, name='conv3d_1')
  >>> print(conv3d)
  >>> tensor = tl.layers.Conv2d(n_filter=32, filter_size=(3, 3, 3), strides=(2, 2, 2), act=tf.nn.relu, name='conv3d_2')(net)
  >>> print(tensor)
class_docstr_tok: ["Simplified","version","of",":","class",":","`","Conv3dLayer","`",".","Parameters","--","--","--","--","--","n_filter",":","int","The","number","of","filters",".","filter_size",":","tuple","of","int","The","filter","size","(","height",",","width",")",".","strides",":","tuple","of","int","The","sliding","window","strides","of","corresponding","input","dimensions",".","It","must","be","in","the","same","order","as","the","``","shape","``","parameter",".","dilation_rate",":","tuple","of","int","Specifying","the","dilation","rate","to","use","for","dilated","convolution",".","act",":","activation","function","The","activation","function","of","this","layer",".","padding",":","str","The","padding","algorithm","type",":","``","SAME","''","or","``","VALID","''",".","data_format",":","str","``","channels_last","''","(","NDHWC",",","default",")","or","``","channels_first","''","(","NCDHW",")",".","W_init",":","initializer","The","initializer","for","the","the","weight","matrix",".","b_init",":","initializer","or","None","The","initializer","for","the","the","bias","vector",".","If","None",",","skip","biases",".","in_channels",":","int","The","number","of","in","channels",".","name",":","None","or","str","A","unique","layer","name",".","Examples","--","--","--","--","With","TensorLayer",">",">",">","net","=","tl.layers.Input","(","[","8",",","20",",","20",",","20",",","3","]",",","name='input","'",")",">",">",">","conv3d","=","tl.layers.Conv2d","(","n_filter=32",",","filter_size=","(","3",",","3",",","3",")",",","strides=","(","2",",","2",",","2",")",",","b_init=None",",","in_channels=3",",","name='conv3d_1","'",")",">",">",">","print","(","conv3d",")",">",">",">","tensor","=","tl.layers.Conv2d","(","n_filter=32",",","filter_size=","(","3",",","3",",","3",")",",","strides=","(","2",",","2",",","2",")",",","act=tf.nn.relu",",","name='conv3d_2","'",")","(","net",")",">",">",">","print","(","tensor",")"]
code: def build(self, inputs_shape):
      if self.data_format == "channels_last":
          self.data_format = "NDHWC"
          if self.in_channels is None:
              self.in_channels = inputs_shape[-1]
          self._strides = [
              1,
              self._strides[0],
              self._strides[1],
              self._strides[2],
              1,
          ]
          self._dilation_rate = [
              1,
              self.dilation_rate[0],
              self.dilation_rate[1],
              self.dilation_rate[2],
              1,
          ]
      elif self.data_format == "channels_first":
          self.data_format = "NCDHW"
          if self.in_channels is None:
              self.in_channels = inputs_shape[1]
          self._strides = [
              1,
              1,
              self._strides[0],
              self._strides[1],
              self._strides[2],
          ]
          self._dilation_rate = [
              1,
              1,
              self._dilation_rate[0],
              self._dilation_rate[1],
              self._dilation_rate[2],
          ]
      else:
          raise Exception(
              "data_format should be either channels_last or channels_first"
          )
  
      self.filter_shape = (
          self.filter_size[0],
          self.filter_size[1],
          self.filter_size[2],
          self.in_channels,
          self.n_filter,
      )
  
      self.W = self._get_weights(
          "filters", shape=self.filter_shape, init=self.W_init
      )
  
      if self.b_init:
          self.b = self._get_weights(
              "biases", shape=(self.n_filter,), init=self.b_init
          )
  
imports: import tensorflow
  import tensorlayer
  from tensorlayer import logging
  from tensorlayer.decorators import deprecated_alias
  from tensorlayer.layers.core import Layer
  from tensorlayer.layers.utils import get_collection_trainable
label_desc: Use image node_id 3 for calling the Conv3d obj's underlying member method code with example usage: obj.build(inputs_shape) without return types