class_docstr: Calculate the calibration curve with brier score for each class.
  
  Parameters
  ----------
  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, n_samples: int = 1_000_000, random_state: int = 42, **kwargs
  ):
      super().__init__(**kwargs)
      self.n_samples = n_samples
      self.random_state = random_state
  
file_path: deepchecks/deepchecks/tabular/checks/model_evaluation/calibration_score.py
imports: import typing
  import plotly.graph_objects
  from sklearn.calibration import calibration_curve
  from sklearn.metrics import brier_score_loss
  from deepchecks.core import CheckResult
  from deepchecks.core.errors import DeepchecksNotSupportedError
  from deepchecks.tabular import Context, SingleDatasetCheck
  from deepchecks.utils.typing import ClassificationModel
label_desc: Use image node_id 1 to create a new CalibrationScore object from inherited base classes: SingleDatasetCheck with example: obj = CalibrationScore(n_samples, random_state)