code: def compute_model(self, inputs, variables, training):
      x, y = [tf.cast(i, tf.float32) for i in inputs]
      w1, w2, b1, b2 = variables
      dense1 = tf.nn.relu(tf.matmul(x, w1) + b1)
      logits = tf.matmul(dense1, w2) + b2
      output = tf.sigmoid(logits)
      loss = tf.reduce_mean(
          tf.nn.sigmoid_cross_entropy_with_logits(
              logits=logits, labels=y
          )
      )
      return loss, [output]
  
file_path: deepchem/examples/low_data/toxcast_maml.py
imports: from __future__ import print_function
  import deepchem
  import numpy
  import tensorflow
  from sklearn.metrics import accuracy_score
label_desc: Use image node_id 2 for calling the ToxcastLearner obj's underlying member method code with example usage: obj.compute_model(inputs, variables, training) and returns: loss,  list