class_docstr: Linear DSModule based on BLAS library and standalone bias + activation kernel implementation.
class_docstr_tok: ["Linear","DSModule","based","on","BLAS","library","and","standalone","bias","+","activation","kernel","implementation","."]
docstr: Return the padded, pre-allocated output Tensor.
docstr_tok: ["Return","the","padded",",","pre-allocated","output","Tensor","."]
code: def output(self) -> torch.Tensor:
      """
      Return the padded, pre-allocated output Tensor.
      """
      return self._output
  
file_path: DeepSpeed/deepspeed/inference/v2/modules/implementations/linear/blas_fp_linear.py
imports: from typing import Any, Dict, Optional
  import torch
  from deepspeed.accelerator import get_accelerator
  from ....allocator import empty_from
  from ....inference_utils import is_gated
  from ....kernels.core_ops import BlasLibLinear, CUDABiasActivation, CUDAGatedActivation
  from ...interfaces import DSLinearBase, DSLinearRegistry
  from ...configs import DSLinearConfig
  from ....inference_parameter import InferenceParameter
label_desc: Use image node_id 6 for calling the BlasFPLinear obj's underlying member method code with example usage: obj.output() and returns: self