model_ft, input_size = initialize_model(model_name, 102, feature_extract, use_pretrained=True)

As an AI language model, I am unable to provide the full implementation of the `initialize_model` function because it is not a standard Python function. However, I can explain what this line of code does and provide some general guidance.

This line of code initializes a deep learning model for image classification. The `model_name` argument specifies the architecture of the model, which can be one of several popular models such as ResNet, Inception, or VGG. The `102` argument specifies the number of classes to be predicted by the model. The `feature_extract` argument specifies whether to fine-tune the entire model or only the final layer. If `feature_extract` is set to `True`, then only the final layer will be trained, and the rest of the model will be frozen. If `use_pretrained` is set to `True`, then the model will be initialized with pre-trained weights, which can improve performance and reduce training time.

The `input_size` variable is set to the size of the input images that the model expects. This will depend on the specific model architecture and can be determined by consulting the documentation or source code.

To use this line of code, you would need to define the `model_name` variable, set `feature_extract` and `use_pretrained` as desired, and then call the `initialize_model` function with these arguments. The function would then return the initialized model and the `input_size` variable.