class_docstr: Base generative model with convenient generation API.
  
  Args:
      name (str): name of the model.
      compression_model (CompressionModel): Compression model
          used to map audio to invertible discrete representations.
      lm (LMModel): Language model over discrete representations.
      max_duration (float, optional): maximum duration the model can produce,
          otherwise, inferred from the training params.
docstr: Generate samples conditioned on text.
  
  Args:
      descriptions (list of str): A list of strings used as text conditioning.
      progress (bool, optional): Flag to display progress of the generation process. Defaults to False.
docstr_tok: ["Generate","samples","conditioned","on","text",".","Args",":","descriptions","(","list","of","str",")",":","A","list","of","strings","used","as","text","conditioning",".","progress","(","bool",",","optional",")",":","Flag","to","display","progress","of","the","generation","process",".","Defaults","to","False","."]
code: def generate(
      self,
      descriptions: tp.List[str],
      progress: bool = False,
      return_tokens: bool = False,
  ) -> tp.Union[torch.Tensor, tp.Tuple[torch.Tensor, torch.Tensor]]:
      """Generate samples conditioned on text.
  
      Args:
          descriptions (list of str): A list of strings used as text conditioning.
          progress (bool, optional): Flag to display progress of the generation process. Defaults to False.
      """
      attributes, prompt_tokens = self._prepare_tokens_and_attributes(
          descriptions, None
      )
      assert prompt_tokens is None
      tokens = self._generate_tokens(
          attributes, prompt_tokens, progress
      )
      if return_tokens:
          return self.generate_audio(tokens), tokens
      return self.generate_audio(tokens)
  
imports: from abc import ABC, abstractmethod
  import typing
  import omegaconf
  import torch
  from .encodec import CompressionModel
  from .lm import LMModel
  from .builders import get_wrapped_compression_model
  from ..data.audio_utils import convert_audio
  from ..modules.conditioners import ConditioningAttributes
  from ..utils.autocast import TorchAutocast
label_desc: Use image node_id 10 for calling the BaseGenModel obj's underlying member method code with example usage: obj.generate(descriptions, progress, return_tokens) and returns: self, self,  tokens