code: def embed(
      inp,
      n_inputs,
      n_features,
      initializer=None,
      fix_parameters=False,
      apply_w=None,
  ):
      """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.
          apply_w (function): Lambda, function, or callable object applied to the weights.
  
      Returns:
          ~nnabla.Variable: Output with shape :math:`(I_0, ..., I_N, W_1, ..., W_M)`
      """
      if not initializer:
          initializer = UniformInitializer((-np.sqrt(3.0), np.sqrt(3)))
      w = get_parameter_or_create(
          "W",
          [n_inputs, n_features],
          initializer,
          True,
          not fix_parameters,
      )
      if apply_w is not None:
          w = apply_w(w)
      return F.embed(inp, w)
  
imports: from six import exec_
  import numpy
  import nnabla
  import nnabla.functions
  from nnabla.parameter import get_parameter_or_create, get_parameter
  from nnabla.initializer import calc_uniform_lim_glorot, ConstantInitializer, NormalInitializer, UniformInitializer, WeightNormalizationScaleInitializer
label_desc: Use image node_id 27 for calling a global function with example usage: embed(inp, n_inputs, n_features, initializer, fix_parameters, apply_w) and returns: F