class_docstr: Check if identifiers (Index/Date) can be used to predict the label.
  
  Parameters
  ----------
  ppscore_params : any , default: None
      dictionary containing params to pass to ppscore predictor
  n_samples : int , default: 1_000_000
      number of samples to use for this check.
  random_state : int, default: 42
      random seed for all check internals.
code: def __init__(
      self,
      ppscore_params=None,
      n_samples: int = 1_000_000,
      random_state: int = 42,
      **kwargs,
  ):
      super().__init__(**kwargs)
      self.ppscore_params = ppscore_params or {}
      self.n_samples = n_samples
      self.random_state = random_state
  
file_path: deepchecks/deepchecks/tabular/checks/data_integrity/identifier_label_correlation.py
imports: from typing import Dict
  import pandas
  import plotly.express
  import deepchecks.ppscore
  from deepchecks.core import CheckResult, ConditionCategory, ConditionResult
  from deepchecks.core.errors import DatasetValidationError
  from deepchecks.tabular import Context, SingleDatasetCheck
  from deepchecks.tabular.dataset import _get_dataset_docs_tag
  from deepchecks.tabular.utils.messages import get_condition_passed_message
  from deepchecks.tabular.utils.task_type import TaskType
  from deepchecks.utils.strings import format_number
label_desc: Use image node_id 1 to create a new IdentifierLabelCorrelation object from inherited base classes: SingleDatasetCheck with example: obj = IdentifierLabelCorrelation(ppscore_params, n_samples, random_state)