code: def __init__(self, config: WhisperConfig):
      super().__init__(config)
      self.model = WhisperModel(config)
      self.proj_out = nn.Linear(
          config.d_model, config.vocab_size, bias=False
      )
  
      # Initialize weights and apply final processing
      self.post_init()
  
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 1 to create a new WhisperForConditionalGeneration object from inherited base classes: WhisperPreTrainedModel with example: obj = WhisperForConditionalGeneration(config)