class_docstr: Evaluator.
  
  A class used for performing predictions and evaluate a model performance.
  
  Arguments:
      tensors: list of `Tensor`. A list of tensors to perform predictions.
      model: `str`. The model weights path (Optional).
      session: `Session`. The session to run the prediction (Optional).
class_docstr_tok: ["Evaluator",".","A","class","used","for","performing","predictions","and","evaluate","a","model","performance",".","Arguments",":","tensors",":","list","of","`","Tensor","`",".","A","list","of","tensors","to","perform","predictions",".","model",":","`","str","`",".","The","model","weights","path","(","Optional",")",".","session",":","`","Session","`",".","The","session","to","run","the","prediction","(","Optional",")","."]
code: def __init__(self, tensors, model=None, session=None):
      self.tensors = to_list(tensors)
      self.graph = self.tensors[0].graph
      self.model = model
      self.dprep_collection = tf.get_collection(tf.GraphKeys.DATA_PREP)
      self.inputs = tf.get_collection(tf.GraphKeys.INPUTS)
  
      with self.graph.as_default():
          self.session = tf.Session()
          if session:
              self.session = session
          self.saver = tf.train.Saver()
          if model:
              self.saver.restore(self.session, model)
  
file_path: tflearn/tflearn/helpers/evaluator.py
imports: from __future__ import division, print_function, absolute_import
  import tensorflow.compat.v1
  import tflearn
  from ..utils import to_list
  from ..None import data_flow
  from ..None import metrics
  from .trainer import evaluate_flow
label_desc: Use image node_id 1 to create a new Evaluator object from inherited base classes: object with example: obj = Evaluator(tensors, model, session)