class_docstr: Applies convolution operator for filtering windows of three-dimensional inputs.
  You can also use Conv3D as an alias of this layer.
  Data format currently supported for this layer is dim_ordering='th' (Channel First).
  The input of this layer should be 5D.
  
  When you use this layer as the first layer of a model, you need to provide the argument
  input_shape (a shape tuple, does not include the batch dimension).
  
  # Arguments
  nb_filter: Number of convolution filters to use.
  kernel_dim1: Length of the first dimension in the convolution kernel.
  kernel_dim2: Length of the second dimension in the convolution kernel.
  kernel_dim3: Length of the third dimension in the convolution kernel.
  init: String representation of the initialization method for the weights of the layer.
        Default is 'glorot_uniform'.
  activation: String representation of the activation function to use
              (such as 'relu' or 'sigmoid'). Default is None.
  border_mode: Either 'valid' or 'same'. Default is 'valid'.
  subsample: Int tuple of length 3. Factor by which to subsample output.
             Also called strides elsewhere. Default is (1, 1, 1).
  dim_ordering: Format of input data. Only 'th' (Channel First) is supported for now.
  W_regularizer: An instance of [[Regularizer]], (eg. L1 or L2 regularization),
                 applied to the input weights matrices. Default is None.
  b_regularizer: An instance of [[Regularizer]], applied to the bias. Default is None.
  bias: Whether to include a bias (i.e. make the layer affine rather than linear).
        Default is True.
  input_shape: A shape tuple, not including batch.
  name: String to set the name of the layer.
        If not specified, its name will by default to be a generated string.
  
  >>> conv3d = Convolution3D(32, 3, 4, 5, input_shape=(3, 64, 64, 64))
  creating: createZooKerasConvolution3D
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code: def __init__(
      self,
      nb_filter,
      kernel_dim1,
      kernel_dim2,
      kernel_dim3,
      init="glorot_uniform",
      activation=None,
      border_mode="valid",
      subsample=(1, 1, 1),
      dim_ordering="th",
      W_regularizer=None,
      b_regularizer=None,
      bias=True,
      input_shape=None,
      **kwargs,
  ):
      super(Convolution3D, self).__init__(
          None,
          nb_filter,
          kernel_dim1,
          kernel_dim2,
          kernel_dim3,
          init,
          activation,
          border_mode,
          subsample,
          dim_ordering,
          W_regularizer,
          b_regularizer,
          bias,
          list(input_shape) if input_shape else None,
          **kwargs,
      )
  
imports: import sys
  from ..engine.topology import ZooKerasLayer
  from bigdl.dllib.utils.log4Error import *
label_desc: Use image node_id 1 to create a new Convolution3D object from inherited base classes: ZooKerasLayer with example: obj = Convolution3D(nb_filter, kernel_dim1, kernel_dim2, kernel_dim3, init, activation, border_mode, subsample, dim_ordering, W_regularizer, b_regularizer, bias, input_shape)