class_docstr: Fetch training curve data from CloudWatch Metrics for a specific training job.
docstr: Initialize a ``TrainingJobAnalytics`` instance.
  
  Args:
      training_job_name (str): name of the TrainingJob to analyze.
      metric_names (list, optional): string names of all the metrics to
          collect for this training job. If not specified, then it will
          use all metric names configured for this job.
      sagemaker_session (sagemaker.session.Session): Session object which
          manages interactions with Amazon SageMaker APIs and any other
          AWS services needed. If not specified, one is specified using
          the default AWS configuration chain.
      start_time:
      end_time:
      period:
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code: def __init__(
      self,
      training_job_name,
      metric_names=None,
      sagemaker_session=None,
      start_time=None,
      end_time=None,
      period=None,
  ):
      """Initialize a ``TrainingJobAnalytics`` instance.
  
      Args:
          training_job_name (str): name of the TrainingJob to analyze.
          metric_names (list, optional): string names of all the metrics to
              collect for this training job. If not specified, then it will
              use all metric names configured for this job.
          sagemaker_session (sagemaker.session.Session): Session object which
              manages interactions with Amazon SageMaker APIs and any other
              AWS services needed. If not specified, one is specified using
              the default AWS configuration chain.
          start_time:
          end_time:
          period:
      """
      sagemaker_session = sagemaker_session or Session()
      self._sage_client = sagemaker_session.sagemaker_client
      self._cloudwatch = sagemaker_session.boto_session.client(
          "cloudwatch"
      )
      self._training_job_name = training_job_name
      self._start_time = start_time
      self._end_time = end_time
      self._period = period or METRICS_PERIOD_DEFAULT
  
      if metric_names:
          self._metric_names = metric_names
      else:
          self._metric_names = self._metric_names_for_training_job()
  
      super(TrainingJobAnalytics, self).__init__()
      self.clear_cache()
  
imports: from __future__ import print_function, absolute_import
  from abc import ABCMeta, abstractmethod
  from collections import defaultdict, OrderedDict
  import datetime
  import logging
  from six import with_metaclass
  from sagemaker.session import Session
  from sagemaker.utils import DeferredError
  from sagemaker.lineage import artifact
label_desc: Use image node_id 1 to create a new TrainingJobAnalytics object from inherited base classes: AnalyticsMetricsBase with example: obj = TrainingJobAnalytics(training_job_name, metric_names, sagemaker_session, start_time, end_time, period)