v0.5.0

• Added tensor shape tracking to support dynamic shapes for flatten, squeeze, unsqueeze, view, reshape, interpolate, and getitem methods
• Added EasyOCR example
• Added the DatasetRecorder context manager, allowing to easily capture of module inputs in large pipeline for calibration and shape inference
• Added support for legacy max_batch_size using optimization profiles
• Added support for nested tuple, dict and list module inputs and outputs via. the Flattener class
• Added ability to accept dataset as inputs argument, and infer optimization profiles from the data
• Added Dataset, TensorBatchDataset, ListDataset, and FolderDatset classes
• Added support for dynamic shapes
  • Known limitation: Currently some converters (ie: View) may have unexpected behavior if their arguments are defined with dynamic Tensor shapes.