docstr: Initialize a T-learner with two MLP models.
docstr_tok: ["Initialize","a","T-learner","with","two","MLP","models","."]
code: def __init__(self, ate_alpha=0.05, control_name=0, *args, **kwargs):
      """Initialize a T-learner with two MLP models."""
      super().__init__(
          learner=MLPRegressor(*args, **kwargs),
          ate_alpha=ate_alpha,
          control_name=control_name,
      )
  
imports: from copy import deepcopy
  import logging
  import numpy
  from packaging import version
  from scipy.stats import norm
  import sklearn
  from sklearn.exceptions import ConvergenceWarning
  from sklearn.neural_network import MLPRegressor
  from tqdm import tqdm
  from xgboost import XGBRegressor
  from causalml.inference.meta.base import BaseLearner
  from causalml.inference.meta.utils import check_treatment_vector, convert_pd_to_np
  from causalml.metrics import regression_metrics, classification_metrics
label_desc: Use image node_id 1 to create a new MLPTRegressor object from inherited base classes: BaseTRegressor with example: obj = MLPTRegressor(ate_alpha, control_name)