class_docstr: Used to merge a list of inputs into a single output, following some merge mode.
  Merge must have at least two input layers.
  
  When using this layer as the first layer in a model, you need to provide the argument
  input_shape for input layers (a list of shape tuples, does not include the batch dimension).
  
  # Arguments
  layers: A list of layer instances. Must be more than one layer.
  mode: Merge mode. String, must be one of: 'sum', 'mul', 'concat', 'ave', 'cos',
        'dot', 'max', 'sub', 'div', 'min'. Default is 'sum'.
  concat_axis: Int, axis to use when concatenating layers.
               Only specify this when merge mode is 'concat'.
               Default is -1, meaning the last axis of the input.
  input_shape: A list of shape tuples, each 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.
  
  >>> l1 = InputLayer(input_shape=(3, 5))
  creating: createZooKerasInputLayer
  >>> l2 = InputLayer(input_shape=(3, 5))
  creating: createZooKerasInputLayer
  >>> merge = Merge(layers=[l1, l2], mode='sum', name="merge1")
  creating: createZooKerasMerge
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code: def __init__(
      self,
      layers=None,
      mode="sum",
      concat_axis=-1,
      input_shape=None,
      **kwargs,
  ):
      super(Merge, self).__init__(
          None,
          list(layers) if layers else None,
          mode,
          concat_axis,
          input_shape,
          **kwargs,
      )
  
imports: import bigdl.dllib.keras.autograd
  from bigdl.dllib.feature.image import ImageSet
  from bigdl.dllib.feature.text import TextSet
  from bigdl.dllib.feature.common import FeatureSet
  from bigdl.dllib.keras.base import ZooKerasLayer
  from bigdl.dllib.keras.utils import *
  from bigdl.dllib.nn.layer import Layer
  from bigdl.dllib.utils.file_utils import callZooFunc
label_desc: Use image node_id 1 to create a new Merge object from inherited base classes: ZooKerasLayer with example: obj = Merge(layers, mode, concat_axis, input_shape)