  parameter names that are not initialized. This is not a problem, as these parameters are batch normalization
  statistics, which are going to have values when fine-tuning on a custom dataset.
If you want to train the model in a distributed environment, make sure to call [synchronize_batch_norm] on the
  model in order to properly synchronize the batch normalization layers of the visual backbone.
In addition, there's LayoutXLM, which is a multilingual version of LayoutLMv2. More information can be found on
LayoutXLM's documentation page.
Usage: LayoutLMv2Processor
The easiest way to prepare data for the model is to use [LayoutLMv2Processor], which internally
combines a feature extractor ([LayoutLMv2FeatureExtractor]) and a tokenizer
([LayoutLMv2Tokenizer] or [LayoutLMv2TokenizerFast]). The feature extractor
