class_docstr: SparseEmbedding is the sparse version of layer Embedding.
  
  The input of SparseEmbedding should be a 2D SparseTensor or two 2D sparseTensors.
  If the input is a SparseTensor, the values are positive integer ids,
  values in each row of this SparseTensor will be turned into a dense vector.
  If the input is two SparseTensors, the first tensor should be the integer ids, just
  like the SparseTensor input. And the second tensor is the corresponding
  weights of the integer ids.
  
  This layer can only be used as the first layer in a model, you need to provide the argument
  inputShape (a Single Shape, does not include the batch dimension).
  
  # Arguments
  input_dim: Size of the vocabulary. Int > 0.
  output_dim: Dimension of the dense embedding. Int >= 0.
  init: String representation of the initialization method for the weights of the layer.
        Default is 'uniform'.
  combiner: A string specifying the reduce type.
            Currently "mean", "sum", "sqrtn" is supported.
  max_norm: If provided, each embedding is normalized to have l2 norm equal to
             maxNorm before combining.
  W_regularizer: An instance of [[Regularizer]], (eg. L1 or L2 regularization),
                 applied to the embedding matrix. Default is None.
  input_shape: A Single Shape, does not include the batch dimension.
  name: String to set the name of the layer.
        If not specified, its name will by default to be a generated string.
  
  >>> sparse_embedding = SparseEmbedding(input_dim=10, output_dim=4, input_shape=(10, ))
  creating: createZooKerasSparseEmbedding
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code: def __init__(
      self,
      input_dim,
      output_dim,
      combiner="sum",
      max_norm=-1.0,
      init="uniform",
      W_regularizer=None,
      input_shape=None,
      **kwargs,
  ):
      super(SparseEmbedding, self).__init__(
          None,
          input_dim,
          output_dim,
          combiner,
          max_norm,
          init,
          W_regularizer,
          list(input_shape) if input_shape else None,
          **kwargs,
      )
  
file_path: BigDL/python/dllib/src/bigdl/dllib/keras/layers/embeddings.py
imports: import sys
  from bigdl.dllib.utils.common import JTensor
  from bigdl.dllib.utils.file_utils import callZooFunc
  from ..engine.topology import ZooKerasLayer
label_desc: Use image node_id 1 to create a new SparseEmbedding object from inherited base classes: ZooKerasLayer with example: obj = SparseEmbedding(input_dim, output_dim, combiner, max_norm, init, W_regularizer, input_shape)