code: def fc_sparse(
      model, blob_in, blob_out, w_csr, iw, jw, bias, **kwargs
  ):
      """FC_Sparse: Only takes in allocated weights"""
      if not (w_csr and iw and jw and bias):
          print("Warning...")
      model.AddParameter(w_csr)
      model.AddParameter(iw)
      model.AddParameter(jw)
      model.AddParameter(bias)
      return model.net.FC_Sparse(
          [blob_in, w_csr, iw, jw, bias], blob_out, **kwargs
      )
  
file_path: pytorch/caffe2/python/helpers/fc.py
imports: from caffe2.python import core
  from caffe2.python.modeling import initializers
  from caffe2.python.modeling.parameter_info import ParameterTags
label_desc: Use image node_id 6 for calling a global function with example usage: fc_sparse(model, blob_in, blob_out, w_csr, iw, jw, bias) and returns: model