class_docstr: The :class:`Embedding` class is a look-up table for word embedding.
  
  Word content are accessed using integer indexes, then the output is the embedded word vector.
  To train a word embedding matrix, you can used :class:`Word2vecEmbedding`.
  If you have a pre-trained matrix, you can assign the parameters into it.
  
  Parameters
  ----------
  vocabulary_size : int
      The size of vocabulary, number of words.
  embedding_size : int
      The number of embedding dimensions.
  E_init : initializer
      The initializer for the embedding matrix.
  E_init_args : dictionary
      The arguments for embedding matrix initializer.
  name : str
      A unique layer name.
  
  Attributes
  ----------
  outputs : tensor
      The embedding layer output is a 3D tensor in the shape: (batch_size, num_steps(num_words), embedding_size).
  
  Examples
  --------
  >>> import tensorflow as tf
  >>> import tensorlayer as tl
  >>> input = tl.layers.Input([8, 100], dtype=tf.int32)
  >>> embed = tl.layers.Embedding(vocabulary_size=1000, embedding_size=50, name='embed')
  >>> print(embed)
  Embedding(vocabulary_size=1000, embedding_size=50)
  >>> tensor = embed(input)
  >>> print(tensor)
  tf.Tensor([...], shape=(8, 100, 50), dtype=float32)
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docstr: Parameters
  ----------
  inputs_shape : tuple
      the shape of inputs tensor
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code: def build(self, inputs_shape):
      """
      Parameters
      ----------
      inputs_shape : tuple
          the shape of inputs tensor
      """
  
      self.embeddings = self._get_weights(
          "embeddings",
          shape=(self.vocabulary_size, self.embedding_size),
          init=self.E_init,
      )
  
imports: import numpy
  import tensorflow
  import tensorlayer
  from tensorlayer import logging
  from tensorlayer.layers.core import Layer
label_desc: Use image node_id 3 for calling the Embedding obj's underlying member method code with example usage: obj.build(inputs_shape) without return types