Transform the images into tf.Tensor objects and use tf.data.Dataset.from_tensors() to create the dataset, ensuring all data is loaded into memory at once for quick access.

Apply the tf.data.Dataset.prefetch transformation to the dataset after loading all images into memory, aiming to improve data loading efficiency by overlapping the preprocessing and model execution.

Convert the images into tf.Tensor objects and utilize Dataset.from_tensor_slices() to create the dataset, assuming the slices will manage memory usage effectively.

Encode the images into TFRecords format, store them in Cloud Storage, and use the tf.data API to stream the data during training, ensuring efficient data retrieval and minimal memory usage.