class_docstr: The LookUp module maps (embeds) input ids into *fixed* representations. The representations are     not be updated during training. The module outputs a tensor with shape     **shape(ids) + [embedding dimensionality]**.
  
  Parameters
  ----------
  embed: numpy array
      Fixed embedding matrix.
  ids: Tensorflow tensor, optional
      List of ids to retrieve embeddings. If *None*, the whole embedding matrix is returned.
  scope: str, optional
      Scope for module variables.
  reuse: bool, optional
      Whether or not to reuse module variables.
code: def __init__(self, embed, ids=None, scope=None, reuse=False):
      assert embed is not None, "embed cannot be None"
      self._embed = embed
      self._ids = ids
      super(LookUp, self).__init__(scope=scope, reuse=reuse)
  
file_path: openrec/openrec/tf1/legacy/modules/extractions/look_up.py
imports: from __future__ import print_function
  import tensorflow
  from termcolor import colored
  from openrec.tf1.legacy.modules.extractions import Extraction
label_desc: Use image node_id 1 to create a new LookUp object from inherited base classes: Extraction with example: obj = LookUp(embed, ids, scope, reuse)