human
Solve the following python programming problem given the following code:
# Model function:
def generate_model_fn(dropout):
  """Return a function that determines how TF estimator operates.

  The estimator has 3 modes of operation:
  * train (fitting and updating the model)
  * eval (collecting and returning validation metrics)
  * predict (using the model to label unlabeled images)

  The returned function _cnn_model_fn below determines what to do depending
  on the mode of operation, and returns specs telling the estimator what to
  execute for that mode.

  Args:
    dropout: regularization parameter in last layer (between 0 and 1, exclusive)

  Returns:
    _cnn_model_fn: a function that returns specs for use with TF estimator
  """

  def _cnn_model_fn(features, labels, mode):
    """A function that determines specs for the TF estimator based on mode of operation.
    
    Args: 
      features: actual data (which goes into scope within estimator function) as 4-d tensor (of batch_size),
                pulled in via tf executing _input_fn(), which is the output to generate_input_fn() and is in memory
      labels: 1-d tensor of 0s and 1s
      mode: TF object indicating whether we're in train, eval, or predict mode.
      
    Returns:
           estim_specs: collections of metrics and tensors that are required for training (e.g. prediction values, loss value, train_op tells model weights how to update)
    """

    # Use the cnn() to compute logits:
    logits_train = cnn(features, dropout, reuse=False, is_training=True)
    logits_eval = cnn(features, dropout, reuse=True, is_training=False)
    # We'll be evaluating these later.

    # Transform logits into predictions:
    pred_classes = tf.argmax(logits_eval, axis=1)  # Returns 0 or 1, whichever has larger logit.
    pred_prob = tf.nn.softmax(logits=logits_eval)[:, 1]  # Applies softmax function to return 2-d probability vector.
    # Note: we're not outputting pred_prob in this tutorial, that line just shows you
    # how to get it if you want it. Softmax[i] = exp(logit[i]) / sum(exp((logit[:]))

    # If we're in...
Given the above code, generate some markdown instructions for it.
assistant
#Here is Python program to compute logits:
.

Model function
The model function tells TensorFlow how to call the model we designed above and what to do when we're in training vs evaluation vs prediction mode.  This is where we define the loss function, the optimizer, and the performance metric (which we picked in Step 1).