class_docstr: A collection of parameters, metrics, and artifacts to create a ML model.
class_docstr_tok: ["A","collection","of","parameters",",","metrics",",","and","artifacts","to","create","a","ML","model","."]
docstr: Verify the length of trial component artifacts
  
  Args:
      is_output (bool): Determines direction of association to the
          trial component.
  
  Raises:
      ValueError: If the length of trial component artifacts exceeds the limit.
docstr_tok: ["Verify","the","length","of","trial","component","artifacts","Args",":","is_output","(","bool",")",":","Determines","direction","of","association","to","the","trial","component",".","Raises",":","ValueError",":","If","the","length","of","trial","component","artifacts","exceeds","the","limit","."]
code: def _verify_trial_component_artifacts_length(self, is_output):
      """Verify the length of trial component artifacts
  
      Args:
          is_output (bool): Determines direction of association to the
              trial component.
  
      Raises:
          ValueError: If the length of trial component artifacts exceeds the limit.
      """
      err_msg_template = (
          "Cannot add more than {} {}_artifacts under run"
      )
      if is_output:
          if (
              len(self._trial_component.output_artifacts)
              >= MAX_RUN_TC_ARTIFACTS_LEN
          ):
              raise ValueError(
                  err_msg_template.format(
                      MAX_RUN_TC_ARTIFACTS_LEN, "output"
                  )
              )
      else:
          if (
              len(self._trial_component.input_artifacts)
              >= MAX_RUN_TC_ARTIFACTS_LEN
          ):
              raise ValueError(
                  err_msg_template.format(
                      MAX_RUN_TC_ARTIFACTS_LEN, "input"
                  )
              )
  
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 15 for calling the Run obj's underlying member method code with example usage: obj._verify_trial_component_artifacts_length(is_output) without return types