class_docstr: Embed.
  
  Embed slices a matrix/tensor with indexing array/tensor. Weights are initialized with :obj:`nnabla.initializer.UniformInitializer` within the range of :math:`-\sqrt{3}` and :math:`\sqrt{3}`.
  
  Args:
      x(~nnabla.Variable): [Integer] Indices with shape :math:`(I_0, ..., I_N)`
      n_inputs : number of possible inputs, words or vocabraries
      n_features : number of embedding features
      fix_parameters (bool): When set to `True`, the embedding weight matrix
          will not be updated.
  
  Returns:
      ~nnabla.Variable: Output with shape :math:`(I_0, ..., I_N, W_1, ..., W_M)`
code: def __call__(self, inp):
      return F.embed(inp, self.W)
  
file_path: nnabla/python/src/nnabla/experimental/parametric_function_class/embed.py
imports: import nnabla
  import nnabla.functions
  import numpy
  from nnabla.initializer import UniformInitializer
  from .module import Module
label_desc: Use image node_id 2 for calling the Embed obj's underlying member method code with example usage: obj.__call__(inp) and returns: F