class_docstr: The base embedder class that all embedder classes inherit.
  
  Args:
      num_embeds (int, optional): The number of embedding elements, e.g.,
          the vocabulary size of a word embedder.
      hparams (dict or HParams, optional): Embedder hyperparameters. Missing
          hyperparamerter will be set to default values. See
          :meth:`default_hparams` for the hyperparameter structure and
          default values.
class_docstr_tok: ["The","base","embedder","class","that","all","embedder","classes","inherit",".","Args",":","num_embeds","(","int",",","optional",")",":","The","number","of","embedding","elements",",","e.g.",",","the","vocabulary","size","of","a","word","embedder",".","hparams","(","dict","or","HParams",",","optional",")",":","Embedder","hyperparameters",".","Missing","hyperparamerter","will","be","set","to","default","values",".","See",":","meth",":","`","default_hparams","`","for","the","hyperparameter","structure","and","default","values","."]
code: def _init_parameterized_embedding(
      self, init_value, num_embeds, hparams
  ):
      self._embedding = embedder_utils.get_embedding(
          hparams, init_value, num_embeds, self.variable_scope
      )
      if hparams.trainable:
          self._add_trainable_variable(self._embedding)
  
      self._num_embeds = shape_list(self._embedding)[0]
  
      self._dim = shape_list(self._embedding)[1:]
      self._dim_rank = len(self._dim)
      if self._dim_rank == 1:
          self._dim = self._dim[0]
  
file_path: texar/texar/tf/modules/embedders/embedder_base.py
imports: import tensorflow
  from texar.tf.module_base import ModuleBase
  from texar.tf.modules.embedders import embedder_utils
  from texar.tf.utils.shapes import shape_list
label_desc: Use image node_id 2 for calling the EmbedderBase obj's underlying member method code with example usage: obj._init_parameterized_embedding(init_value, num_embeds, hparams) without return types