class_docstr: Training statistics
  
  Attributes:
      training_loss (List): List of training losses
      training_accuracy (List): List of training accuracies
      value_loss (List): List of value losses
      validation_loss (List): List of validation losses
      validation_accuracy (List): List of validation accuracies
class_docstr_tok: ["Training","statistics","Attributes",":","training_loss","(","List",")",":","List","of","training","losses","training_accuracy","(","List",")",":","List","of","training","accuracies","value_loss","(","List",")",":","List","of","value","losses","validation_loss","(","List",")",":","List","of","validation","losses","validation_accuracy","(","List",")",":","List","of","validation","accuracies"]
docstr: Load the stats
docstr_tok: ["Load","the","stats"]
code: def load(
      self,
  ):
      """Load the stats"""
      with open(self.path, "r") as f:
          stats = json.load(f)
      self.training_loss = stats["training_loss"]
      self.training_accuracy = stats["training_accuracy"]
      self.value_loss = stats["value_loss"]
      self.validation_loss = stats["validation_loss"]
      self.validation_accuracy = stats["validation_accuracy"]
  
file_path: nebuly/optimization/chatllama/chatllama/rlhf/utils.py
imports: import json
  import os
  from beartype import beartype
  from plotly import graph_objects
label_desc: Use image node_id 4 for calling the TrainingStats obj's underlying member method code with example usage: obj.load() without return types