class_docstr: Attention output parameter container.
  
  Note: The differentiation for something like GQA for this matrix is primarily
  encompassed in the sharding logic, which is currently expected to be performed by
  the model implementation.
class_docstr_tok: ["Attention","output","parameter","container",".","Note",":","The","differentiation","for","something","like","GQA","for","this","matrix","is","primarily","encompassed","in","the","sharding","logic",",","which","is","currently","expected","to","be","performed","by","the","model","implementation","."]
code: def finalize(self) -> torch.Tensor:
      return self.inference_model.transform_attn_out_param(self.params)
  
imports: import torch
  from ...model_implementations.parameter_base import ParameterBase
label_desc: Use image node_id 1 for calling the AttentionOutputParameter obj's underlying member method code with example usage: obj.finalize() and returns: self