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# Custom Layers and Utilities

This page lists all the custom layers used by the library, as well as the utility functions and classes it provides for modeling.

Most of those are only useful if you are studying the code of the models in the library.

## Layers

[[autodoc]] GradientCheckpointingLayer

## Attention Functions

[[autodoc]] AttentionInterface
    - register

## Attention Mask Functions

[[autodoc]] AttentionMaskInterface
    - register

## Rotary Position Embedding Functions

[[autodoc]] dynamic_rope_update

## Pytorch custom modules

[[autodoc]] pytorch_utils.Conv1D

## PyTorch Helper Functions

[[autodoc]] pytorch_utils.apply_chunking_to_forward

[[autodoc]] pytorch_utils.find_pruneable_heads_and_indices

[[autodoc]] pytorch_utils.prune_layer

[[autodoc]] pytorch_utils.prune_conv1d_layer

[[autodoc]] pytorch_utils.prune_linear_layer

## TensorFlow custom layers

[[autodoc]] modeling_tf_utils.TFConv1D

[[autodoc]] modeling_tf_utils.TFSequenceSummary

## TensorFlow loss functions

[[autodoc]] modeling_tf_utils.TFCausalLanguageModelingLoss

[[autodoc]] modeling_tf_utils.TFMaskedLanguageModelingLoss

[[autodoc]] modeling_tf_utils.TFMultipleChoiceLoss

[[autodoc]] modeling_tf_utils.TFQuestionAnsweringLoss

[[autodoc]] modeling_tf_utils.TFSequenceClassificationLoss

[[autodoc]] modeling_tf_utils.TFTokenClassificationLoss

## TensorFlow Helper Functions

[[autodoc]] modeling_tf_utils.get_initializer

[[autodoc]] modeling_tf_utils.keras_serializable

[[autodoc]] modeling_tf_utils.shape_list
