class_docstr: Base optimizer class from which all other optimizer types are derived.
class_docstr_tok: ["Base","optimizer","class","from","which","all","other","optimizer","types","are","derived","."]
docstr: Transformation to apply if matching was successful.
  
  Transform should return a boolean value indicating if the model graph was altered (by adding/removing nodes).
  
  Args:
      model (ModelGraph): Model to optimize
      node (Layer): The matched node in the model graph.
docstr_tok: ["Transformation","to","apply","if","matching","was","successful",".","Transform","should","return","a","boolean","value","indicating","if","the","model","graph","was","altered","(","by","adding\/removing","nodes",")",".","Args",":","model","(","ModelGraph",")",":","Model","to","optimize","node","(","Layer",")",":","The","matched","node","in","the","model","graph","."]
code: def transform(self, model, node):
      """Transformation to apply if matching was successful.
  
      Transform should return a boolean value indicating if the model graph was altered (by adding/removing nodes).
  
      Args:
          model (ModelGraph): Model to optimize
          node (Layer): The matched node in the model graph.
      """
      raise NotImplementedError
  
imports: import importlib
  import inspect
  import os
  from hls4ml.utils.string_utils import convert_to_snake_case
label_desc: Use image node_id 3 for calling the OptimizerPass obj's underlying member method code with example usage: obj.transform(model, node) without return types