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: Converts param to same data type as input and output.
  
  Parameters:
      param (torch.Tensor): Weight or bias tensor.
docstr_tok: ["Converts","param","to","same","data","type","as","input","and","output",".","Parameters",":","param","(","torch.Tensor",")",":","Weight","or","bias","tensor","."]
code: def transform_param(self, param: torch.Tensor) -> InferenceParameter:
      """
      Converts param to same data type as input and output.
  
      Parameters:
          param (torch.Tensor): Weight or bias tensor.
      """
      param = param.to(self._config.output_dtype)
      return InferenceParameter.initialize(param)
  
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 4 for calling the BlasFPLinear obj's underlying member method code with example usage: obj.transform_param(param) and returns: InferenceParameter