class_docstr: To test the basic functionality of the CausalML estimators
code: def test_causalml_MLPTRegressor(self, init_data):
      # Defined a linear dataset with a given set of properties
      data = init_data
  
      # Create a model that captures the same
      model = CausalModel(
          data=data["df"],
          treatment=data["treatment_name"],
          outcome=data["outcome_name"],
          effect_modifiers=data["effect_modifier_names"],
          graph=data["gml_graph"],
      )
  
      # Identify the effects within the model
      identified_estimand = model.identify_effect(
          proceed_when_unidentifiable=True
      )
  
      mlpt_estimate = model.estimate_effect(
          identified_estimand,
          method_name="backdoor.causalml.inference.meta.MLPTRegressor",
          method_params={
              "init_params": {
                  "hidden_layer_sizes": (10, 10),
                  "learning_rate_init": 0.1,
                  "early_stopping": True,
              }
          },
      )
  
      print("The MLPT estimate obtained:")
      print(mlpt_estimate)
  
file_path: dowhy/tests/causal_estimators/test_causalml_estimator.py
imports: import pytest
  from pytest import mark
  from dowhy import CausalModel
  from dowhy.datasets import linear_dataset
  from xgboost import XGBRegressor
label_desc: Use image node_id 3 for calling the TestCausalmlEstimator obj's underlying member method code with example usage: obj.test_causalml_MLPTRegressor(init_data) without return types