class_docstr: Applies average pooling operation for temporal data.
  The input of this layer should be 3D.
  
  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
  pool_length: Size of the region to which max pooling is applied.
  strides: Factor by which to downscale. 2 will halve the input.
           Default is None, and in this case it will be equal to pool_length..
  border_mode: Either 'valid' or 'same'. Default is 'valid'.
  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.
  
  >>> averagepooling1d = AveragePooling1D(input_shape=(3, 24))
  creating: createZooKerasAveragePooling1D
code: def __init__(
      self,
      pool_length=2,
      stride=None,
      border_mode="valid",
      input_shape=None,
      **kwargs,
  ):
      if not stride:
          stride = -1
      super(AveragePooling1D, self).__init__(
          None,
          pool_length,
          stride,
          border_mode,
          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 AveragePooling1D object from inherited base classes: ZooKerasLayer with example: obj = AveragePooling1D(pool_length, stride, border_mode, input_shape)