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Optimizing a deep learning framework involves implementing various techniques and strategies, which can be quite extensive and specific to your model and use case. However, I can provide you with a simple example of how you might apply mixed precision training using TensorFlow to optimize a deep learning model. Mixed precision training involves using lower precision data types (e.g., float16) for certain layers to reduce memory usage and increase computational throughput.

Please note that this example is simplified and may not cover all optimization techniques. You'll need to adapt and combine multiple strategies for a comprehensive optimization approach.

```python
import tensorflow as tf
from tensorflow.keras.layers import Dense, Flatten
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.mixed_precision import experimental as mixed_precision

# Enable mixed precision training
policy = mixed_precision.Policy('mixed_float16')
mixed_precision.set_policy(policy)

# Create a simple neural network model
model = Sequential([
    Flatten(input_shape=(28, 28)),
    Dense(128, activation='relu'),
    Dense(64, activation='relu'),
    Dense(10, activation='softmax')
])

# Compile the model with mixed precision optimizer
opt = Adam(learning_rate=1e-3)
model.compile(loss='sparse_categorical_crossentropy', optimizer=opt, metrics=['accuracy'])

# Load dataset (MNIST as an example)
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

# Train the model
model.fit(x_train, y_train, epochs=5, batch_size=64, validation_data=(x_test, y_test))

# Convert the model to float32 for inference
model = tf.keras.models.clone_model(model)
model.set_weights(model.get_weights())
```

In this example:
- We enable mixed precision training using TensorFlow's mixed precision API.
- We create a simple neural network model for MNIST digit classification.
- We compile the model with an optimizer that supports mixed precision training.
- The model is trained on the MNIST dataset with mixed precision enabled.
- Finally, we convert the trained model to float32 for inference.

This is just one aspect of optimization, and real-world optimization might involve additional techniques like tensor optimization, parallelism, memory management, and more. The actual optimization strategies will depend on your specific use case and deep learning framework.