class_docstr: lasagne.layers.EmbeddingLayer(incoming, input_size, output_size,
  W=lasagne.init.Normal(), **kwargs)
  
  A layer for word embeddings. The input should be an integer type
  Tensor variable.
  
  Parameters
  ----------
  incoming : a :class:`Layer` instance or a tuple
      The layer feeding into this layer, or the expected input shape.
  
  input_size: int
      The Number of different embeddings. The last embedding will have index
      input_size - 1.
  
  output_size : int
      The size of each embedding.
  
  W : Theano shared variable, expression, numpy array or callable
      Initial value, expression or initializer for the embedding matrix.
      This should be a matrix with shape ``(input_size, output_size)``.
      See :func:`lasagne.utils.create_param` for more information.
  
  Examples
  --------
  >>> from lasagne.layers import EmbeddingLayer, InputLayer, get_output
  >>> import theano
  >>> x = T.imatrix()
  >>> l_in = InputLayer((3, ))
  >>> W = np.arange(3*5).reshape((3, 5)).astype('float32')
  >>> l1 = EmbeddingLayer(l_in, input_size=3, output_size=5, W=W)
  >>> output = get_output(l1, x)
  >>> f = theano.function([x], output)
  >>> x_test = np.array([[0, 2], [1, 2]]).astype('int32')
  >>> np.set_printoptions(formatter={'float_kind': lambda x: '%2.0f.' % x})
  >>> f(x_test)
  array([[[ 0.,  1.,  2.,  3.,  4.],
          [10., 11., 12., 13., 14.]],
  <BLANKLINE>
         [[ 5.,  6.,  7.,  8.,  9.],
          [10., 11., 12., 13., 14.]]], dtype=float32)
code: def get_output_shape_for(self, input_shape):
      return input_shape + (self.output_size,)
  
file_path: Lasagne/lasagne/layers/embedding.py
imports: import numpy
  import theano.tensor
  from ..None import init
  from .base import Layer
label_desc: Use image node_id 2 for calling the EmbeddingLayer obj's underlying member method code with example usage: obj.get_output_shape_for(input_shape) and returns: unknown