class_docstr: Torch Audio Hubert encoder module.
  
  Args:
      extractor_mode: Operation mode of feature extractor.
          Valid values are "group_norm" or "layer_norm".
      extractor_conv_layer_config: Configuration of convolution layers in feature
          extractor. List of convolution configuration,
          i.e. [[output_channel, kernel_size, stride], ...]
      extractor_conv_bias: Whether to include bias term to each convolution
          operation.
      encoder_embed_dim: The dimension of embedding in encoder.
      encoder_projection_dropout: The dropout probability applied after the input
          feature is projected to "encoder_embed_dim".
      encoder_pos_conv_kernel: Kernel size of convolutional positional embeddings.
      encoder_pos_conv_groups: Number of groups of convolutional positional
          embeddings.
      encoder_num_layers: Number of self attention layers in transformer block.
      encoder_num_heads: Number of heads in self attention layers.
      encoder_attention_dropout: Dropout probability applied after softmax in
          self-attention layer.
      encoder_ff_interm_features: Dimension of hidden features in feed forward layer.
      encoder_ff_interm_dropout: Dropout probability applied in feedforward layer.
      encoder_dropout: Dropout probability applied at the end of feed forward layer.
      encoder_layer_norm_first: Control the order of layer norm in transformer layer
          and each encoder layer. If True, in transformer layer, layer norm is
          applied before features are fed to encoder layers.
      encoder_layer_drop: Probability to drop each encoder layer during training.
      mask_prob: Probability for each token to be chosen as start of the span
          to be masked.
      mask_selection: How to choose the mask length.
          Options: [static, uniform, normal, poisson].
      mask_other: Secondary mask argument (used for more complex distributions).
      mask_length: The lengths of the mask.
      no_mask_overlap: Whether to allow masks to overlap.
      mask_min_space: Minimum space between spans (if no overlap is enabled).
      mask_channel_prob: (float): The probability of replacing a feature with 0.
      mask_channel_selection: How to choose the mask length for channel masking.
          Options: [static, uniform, normal, poisson].
      mask_channel_other: Secondary mask argument for channel masking(used for more
          complex distributions).
      mask_channel_length: Minimum space between spans (if no overlap is enabled)
          for channel masking.
      no_mask_channel_overlap: Whether to allow channel masks to overlap.
      mask_channel_min_space: Minimum space between spans for channel
          masking(if no overlap is enabled).
      skip_masked: If True, skip computing losses over masked frames.
      skip_nomask: If True, skip computing losses over unmasked frames.
      num_classes: The number of classes in the labels.
      final_dim: Project final representations and targets to final_dim.
      feature_grad_mult: The factor to scale the convolutional feature extraction
          layer gradients by. The scale factor will not affect the forward pass.
      finetuning: Whether to finetuning the model with ASR or other tasks.
      freeze_encoder_updates: The number of steps to freeze the encoder parameters
          in ASR finetuning.
  Hubert specific Args:
      Please refer to:
      https://pytorch.org/audio/stable/generated/torchaudio.models.hubert_pretrain_model.html#torchaudio.models.hubert_pretrain_model
code: def reload_pretrained_parameters(self):
      self.hubert_pretrain_model.load_state_dict(
          self.pretrained_params, strict=False
      )
      logging.info("Pretrained Hubert model parameters reloaded!")
  
imports: import contextlib
  import copy
  import logging
  import os
  from pathlib import Path
  from typing import List, Optional, Tuple
  import torch
  import yaml
  from filelock import FileLock
  from typeguard import check_argument_types
  from espnet2.asr.encoder.abs_encoder import AbsEncoder
  from espnet.nets.pytorch_backend.nets_utils import make_pad_mask
  from espnet.nets.pytorch_backend.transformer.layer_norm import LayerNorm
label_desc: Use image node_id 7 for calling the TorchAudioHuBERTPretrainEncoder obj's underlying member method code with example usage: obj.reload_pretrained_parameters() without return types