code: def test_all_hyperparameters(sagemaker_session):
      lr = LinearLearner(
          sagemaker_session=sagemaker_session,
          binary_classifier_model_selection_criteria="accuracy",
          target_recall=0.5,
          target_precision=0.6,
          positive_example_weight_mult=0.1,
          epochs=1,
          use_bias=True,
          num_models=5,
          num_calibration_samples=6,
          init_method="uniform",
          init_scale=0.1,
          init_sigma=0.001,
          init_bias=0,
          optimizer="sgd",
          loss="logistic",
          wd=0.4,
          l1=0.04,
          momentum=0.1,
          learning_rate=0.001,
          beta_1=0.2,
          beta_2=0.03,
          bias_lr_mult=5.5,
          bias_wd_mult=6.6,
          use_lr_scheduler=False,
          lr_scheduler_step=2,
          lr_scheduler_factor=0.03,
          lr_scheduler_minimum_lr=0.001,
          normalize_data=False,
          normalize_label=True,
          unbias_data=True,
          unbias_label=False,
          num_point_for_scaler=3,
          margin=1.0,
          quantile=0.5,
          loss_insensitivity=0.1,
          huber_delta=0.1,
          early_stopping_patience=3,
          early_stopping_tolerance=0.001,
          num_classes=1,
          accuracy_top_k=3,
          f_beta=1.0,
          balance_multiclass_weights=False,
          **ALL_REQ_ARGS,
      )
  
      assert lr.hyperparameters() == dict(
          predictor_type="binary_classifier",
          binary_classifier_model_selection_criteria="accuracy",
          target_recall="0.5",
          target_precision="0.6",
          positive_example_weight_mult="0.1",
          epochs="1",
          use_bias="True",
          num_models="5",
          num_calibration_samples="6",
          init_method="uniform",
          init_scale="0.1",
          init_sigma="0.001",
          init_bias="0.0",
          optimizer="sgd",
          loss="logistic",
          wd="0.4",
          l1="0.04",
          momentum="0.1",
          learning_rate="0.001",
          beta_1="0.2",
          beta_2="0.03",
          bias_lr_mult="5.5",
          bias_wd_mult="6.6",
          use_lr_scheduler="False",
          lr_scheduler_step="2",
          lr_scheduler_factor="0.03",
          lr_scheduler_minimum_lr="0.001",
          normalize_data="False",
          normalize_label="True",
          unbias_data="True",
          unbias_label="False",
          num_point_for_scaler="3",
          margin="1.0",
          quantile="0.5",
          loss_insensitivity="0.1",
          huber_delta="0.1",
          early_stopping_patience="3",
          early_stopping_tolerance="0.001",
          num_classes="1",
          accuracy_top_k="3",
          f_beta="1.0",
          balance_multiclass_weights="False",
      )
  
imports: from __future__ import absolute_import
  import pytest
  from mock import Mock, patch
  from sagemaker import image_uris
  from sagemaker.amazon.linear_learner import LinearLearner, LinearLearnerPredictor
  from sagemaker.amazon.amazon_estimator import RecordSet
  from sagemaker.session_settings import SessionSettings
label_desc: Use image node_id 4 for calling a global function with example usage: test_all_hyperparameters(sagemaker_session) without return types