class_docstr: A collection of parameters, metrics, and artifacts to create a ML model.
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docstr: Record a custom scalar metric value for this run.
  
  Note:
       This method is for manual custom metrics, for automatic metrics see the
       ``enable_sagemaker_metrics`` parameter on the ``estimator`` class.
  
  Args:
      name (str): The name of the metric.
      value (float): The value of the metric.
      timestamp (datetime.datetime): The timestamp of the metric.
          If not specified, the current UTC time will be used.
      step (int): The integer iteration number of the metric value (default: None).
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code: def log_metric(
      self,
      name: str,
      value: float,
      timestamp: Optional[datetime.datetime] = None,
      step: Optional[int] = None,
  ):
      """Record a custom scalar metric value for this run.
  
      Note:
           This method is for manual custom metrics, for automatic metrics see the
           ``enable_sagemaker_metrics`` parameter on the ``estimator`` class.
  
      Args:
          name (str): The name of the metric.
          value (float): The value of the metric.
          timestamp (datetime.datetime): The timestamp of the metric.
              If not specified, the current UTC time will be used.
          step (int): The integer iteration number of the metric value (default: None).
      """
      if self._is_input_valid("metric", name, value):
          self._metrics_manager.log_metric(
              metric_name=name,
              value=value,
              timestamp=timestamp,
              step=step,
          )
  
imports: from __future__ import absolute_import
  import datetime
  import logging
  from enum import Enum
  from math import isnan, isinf
  from numbers import Number
  from typing import Optional, List, Dict, TYPE_CHECKING, Union
  import dateutil
  from numpy import array
  from sagemaker.apiutils import _utils
  from sagemaker.experiments import _api_types
  from sagemaker.experiments._api_types import TrialComponentArtifact, _TrialComponentStatusType
  from sagemaker.experiments._helper import _ArtifactUploader, _LineageArtifactTracker, _DEFAULT_ARTIFACT_PREFIX
  from sagemaker.experiments._environment import _RunEnvironment
  from sagemaker.experiments._run_context import _RunContext
  from sagemaker.experiments.experiment import Experiment
  from sagemaker.experiments._metrics import _MetricsManager
  from sagemaker.experiments.trial import _Trial
  from sagemaker.experiments.trial_component import _TrialComponent
  from sagemaker.utils import get_module, unique_name_from_base, format_tags, Tags, TagsDict
  from sagemaker.experiments._utils import guess_media_type, resolve_artifact_name, verify_length_of_true_and_predicted, validate_invoked_inside_run_context, get_tc_and_exp_config_from_job_env, verify_load_input_names, is_run_trial_component
label_desc: Use image node_id 5 for calling the Run obj's underlying member method code with example usage: obj.log_metric(name, value, timestamp, step) without return types