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: Initialize the training stats
  
  Args:
      path (str): Path to save the stats
docstr_tok: ["Initialize","the","training","stats","Args",":","path","(","str",")",":","Path","to","save","the","stats"]
code: def __init__(self, path: str):
      """Initialize the training stats
  
      Args:
          path (str): Path to save the stats
      """
      self.training_loss = []
      self.training_accuracy = []
      self.value_loss = []
      self.validation_loss = []
      self.validation_accuracy = []
      self.path = path
  
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 1 to create a new TrainingStats object  with example: obj = TrainingStats(path)