docstr: Calling this function will disable the gradient computation for the Whisper encoder so that its parameters will
  not be updated during training.
docstr_tok: ["Calling","this","function","will","disable","the","gradient","computation","for","the","Whisper","encoder","so","that","its","parameters","will","not","be","updated","during","training","."]
code: def freeze_encoder(self):
      """
      Calling this function will disable the gradient computation for the Whisper encoder so that its parameters will
      not be updated during training.
      """
      self.model.encoder._freeze_parameters()
  
imports: import math
  import random
  from typing import Optional, Tuple
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import CrossEntropyLoss
  from ...activations import ACT2FN
  from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_whisper import WhisperConfig
label_desc: Use image node_id 7 for calling the WhisperForConditionalGeneration obj's underlying member method code with example usage: obj.freeze_encoder() without return types