code: def _build_graph(self):
      inputs = Input(
          dtype=tf.float32, shape=(self.feature_dim,), name="Input"
      )
      out1 = Dense(units=self.hidden_layer_size, activation="relu")(
          inputs
      )
  
      final = Dense(units=self.n_tasks, activation="sigmoid")(out1)
      outputs = [final]
      output_types = ["prediction"]
      loss = dc.models.losses.BinaryCrossEntropy()
  
      model = tf.keras.Model(inputs=[inputs], outputs=outputs)
      return model, loss, output_types
  
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 2 for calling the MLP obj's underlying member method code with example usage: obj._build_graph() and returns: model,  loss,  output_types