docstr: Constructor for MetricsCaching class
  
      MetricsCaching class will hold Metric objects in a cache that can be edited and read from.
      The class also has the ability to parse a yaml file into Metrics objects
  
  Parameters
  ----------
  config_file_path: str
      Name of file to be parsed
docstr_tok: ["Constructor","for","MetricsCaching","class","MetricsCaching","class","will","hold","Metric","objects","in","a","cache","that","can","be","edited","and","read","from",".","The","class","also","has","the","ability","to","parse","a","yaml","file","into","Metrics","objects","Parameters","--","--","--","--","--","config_file_path",":","str","Name","of","file","to","be","parsed"]
code: def __init__(self, config_file_path):
      """
      Constructor for MetricsCaching class
  
          MetricsCaching class will hold Metric objects in a cache that can be edited and read from.
          The class also has the ability to parse a yaml file into Metrics objects
  
      Parameters
      ----------
      config_file_path: str
          Name of file to be parsed
  
      """
      self.cache = {}
      self.store = []
      self.request_ids = None
      self.model_name = None
      self.config_file_path = config_file_path
      try:
          os.path.exists(self.config_file_path)
      except Exception as exc:
          raise merrors.MetricsCacheTypeError(
              f"Error loading {config_file_path} file: {exc}"
          )
  
imports: import abc
  import os
  import ts.metrics.metric_cache_errors
  from ts.metrics.dimension import Dimension
  from ts.metrics.metric_abstract import MetricAbstract
  from ts.metrics.metric_type_enum import MetricTypes
label_desc: Use image node_id 1 to create a new MetricCacheAbstract object  with example: obj = MetricCacheAbstract(config_file_path)