class_docstr: Convolutional LSTM for 3D input.
  The convolution kernel for this layer is a cubic kernel with equal strides for all dimensions.
  The input of this layer should be 6D, i.e. (samples, time, channels, dim1, dim2, dim3),
  and 'CHANNEL_FIRST' (dimOrdering='th') is expected.
  
  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.
  nb_kernel: Length of the first, second and third dimensions in the convolution kernel.
             Cubic kernel.
  dim_ordering: Format of input data. Only 'th' (Channel First) is supported for now.
  border_mode: Only 'same' is supported for now.
  subsample: Tuple of length 3. Factor by which to subsample output.
             Also called strides elsewhere. Default is (1, 1, 1).
             Only support subsample[0] equal to subsample[1] equal to subsample[2] for now.
  border_mode: One of "same" or "valid". Also called padding elsewhere. Default is "valid".
  W_regularizer: An instance of [[Regularizer]], (eg. L1 or L2 regularization),
                 applied to the input weights matrices. Default is None.
  U_regularizer: An instance of [[Regularizer]], applied the recurrent weights matrices.
                 Default is None.
  b_regularizer: An instance of [[Regularizer]], applied to the bias. Default is None.
  return_sequences: Whether to return the full sequence or only return the last output
                    in the output sequence. Default is False.
  go_backwards: Whether the input sequence will be processed backwards. Default is False.
  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.
  
  >>> convlstm3d = ConvLSTM3D(10, 4, input_shape=(8, 4, 10, 32, 32))
  creating: createZooKerasConvLSTM3D
code: def __init__(
      self,
      nb_filter,
      nb_kernel,
      dim_ordering="th",
      border_mode="valid",
      subsample=(1, 1, 1),
      W_regularizer=None,
      U_regularizer=None,
      b_regularizer=None,
      return_sequences=False,
      go_backwards=False,
      input_shape=None,
      **kwargs,
  ):
      if dim_ordering != "th":
          raise ValueError(
              "For ConvLSTM3D, only dim_ordering='th' is supported for now"
          )
      if border_mode != "same" and border_mode != "valid":
          raise ValueError(
              "For ConvLSTM3D, only support border_mode as 'same' and 'valid'"
          )
      if subsample[0] != subsample[1] or subsample[1] != subsample[2]:
          raise ValueError(
              "For ConvLSTM3D, only equal strides is supported for now"
          )
      super(ConvLSTM3D, self).__init__(
          None,
          nb_filter,
          nb_kernel,
          subsample[0],
          border_mode,
          W_regularizer,
          U_regularizer,
          b_regularizer,
          return_sequences,
          go_backwards,
          list(input_shape) if input_shape else None,
          **kwargs,
      )
  
file_path: analytics-zoo/pyzoo/zoo/pipeline/api/keras/layers/convolutional_recurrent.py
imports: import sys
  from ..engine.topology import ZooKerasLayer
label_desc: Use image node_id 1 to create a new ConvLSTM3D object from inherited base classes: ZooKerasLayer with example: obj = ConvLSTM3D(nb_filter, nb_kernel, dim_ordering, border_mode, subsample, W_regularizer, U_regularizer, b_regularizer, return_sequences, go_backwards, input_shape)