code: def __init__(
      self, n_tasks=1, feature_dim=100, hidden_layer_size=64, **kwargs
  ):
      self.feature_dim = feature_dim
      self.hidden_layer_size = hidden_layer_size
      self.n_tasks = n_tasks
  
      model, loss, output_types = self._build_graph()
      super(MLP, self).__init__(
          model=model, loss=loss, output_types=output_types, **kwargs
      )
  
file_path: deepchem/examples/pretraining/fcnet_pretraining.py
imports: import deepchem
  import numpy
  import tensorflow
  from deepchem.models.losses import L2Loss
  from tensorflow.keras.layers import Input, Dense
label_desc: Use image node_id 1 to create a new MLP object from inherited base classes: dc.models with example: obj = MLP(n_tasks, feature_dim, hidden_layer_size)