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Solve the following python programming problem given the following code:
# Define the contrastive model with model-subclassing
class ContrastiveModel(keras.Model):
    def __init__(self):
        super().__init__()

        self.temperature = temperature
        self.contrastive_augmenter = get_augmenter(**contrastive_augmentation)
        self.classification_augmenter = get_augmenter(**classification_augmentation)
        self.encoder = get_encoder()
        # Non-linear MLP as projection head
        self.projection_head = keras.Sequential(
            [
                keras.Input(shape=(width,)),
                layers.Dense(width, activation="relu"),
                layers.Dense(width),
            ],
            name="projection_head",
        )
        # Single dense layer for linear probing
        self.linear_probe = keras.Sequential(
            [layers.Input(shape=(width,)), layers.Dense(10)], name="linear_probe"
        )

        self.encoder.summary()
        self.projection_head.summary()
        self.linear_probe.summary()

    def compile(self, contrastive_optimizer, probe_optimizer, **kwargs):
        super().compile(**kwargs)

        self.contrastive_optimizer = contrastive_optimizer
        self.probe_optimizer = probe_optimizer

        # self.contrastive_loss will be defined as a method
        self.probe_loss = keras.losses.SparseCategoricalCrossentropy(from_logits=True)

        self.contrastive_loss_tracker = keras.metrics.Mean(name="c_loss")
        self.contrastive_accuracy = keras.metrics.SparseCategoricalAccuracy(
            name="c_acc"
        )
        self.probe_loss_tracker = keras.metrics.Mean(name="p_loss")
        self.probe_accuracy = keras.metrics.SparseCategoricalAccuracy(name="p_acc")

    @property
    def metrics(self):
        return [
            self.contrastive_loss_tracker,
            self.contrastive_accuracy,
            self.probe_loss_tracker,
            self.probe_accuracy,
        ]

    def contrastive_loss(self, projections_1, projections_2):
        # InfoNCE loss (information noise-contrastive estimation)
        #...
Given the above code, generate some markdown instructions for it.
assistant
#Here is the Python program you requested.

Self-supervised model for contrastive pretraining
We pretrain an encoder on unlabeled images with a contrastive loss.
A nonlinear projection head is attached to the top of the encoder, as it
improves the quality of representations of the encoder.
We use the InfoNCE/NT-Xent/N-pairs loss, which can be interpreted in the
following way:

We treat each image in the batch as if it had its own class.
Then, we have two examples (a pair of augmented views) for each "class".
Each view's representation is compared to every possible pair's one (for both
  augmented versions).
We use the temperature-scaled cosine similarity of compared representations as
  logits.
Finally, we use categorical cross-entropy as the "classification" loss

The following two metrics are used for monitoring the pretraining performance:

Contrastive accuracy (SimCLR Table 5):
Self-supervised metric, the ratio of cases in which the representation of an
image is more similar to its differently augmented version's one, than to the
representation of any other image in the current batch. Self-supervised
metrics can be used for hyperparameter tuning even in the case when there are
no labeled examples.
Linear probing accuracy: Linear probing is
a popular metric to evaluate self-supervised classifiers. It is computed as
the accuracy of a logistic regression classifier trained on top of the
encoder's features. In our case, this is done by training a single dense layer
on top of the frozen encoder. Note that contrary to traditional approach where
the classifier is trained after the pretraining phase, in this example we
train it during pretraining. This might slightly decrease its accuracy, but
that way we can monitor its value during training, which helps with
experimentation and debugging.

Another widely used supervised metric is the
KNN accuracy, which is the accuracy of a KNN
classifier trained on top of the encoder's features, which is not implemented in
this example.