code: def _elementwise_linear(
      model,
      op_call,
      blob_in,
      blob_out,
      dim,
      weight_init=None,
      bias_init=None,
      **kwargs,
  ):
      """Elementwise_Linear"""
      weight_init = weight_init or ("ConstantFill", {"value": 1.0})
      bias_init = bias_init or ("ConstantFill", {"value": 0.0})
      blob_out = blob_out or model.net.NextName()
      if model.init_params:
          weight = model.param_init_net.__getattr__(weight_init[0])(
              [], blob_out + "_w", shape=[dim], **weight_init[1]
          )
          bias = model.param_init_net.__getattr__(bias_init[0])(
              [], blob_out + "_b", shape=[dim], **bias_init[1]
          )
      else:
          weight = core.ScopedBlobReference(
              blob_out + "_w", model.param_init_net
          )
          bias = core.ScopedBlobReference(
              blob_out + "_b", model.param_init_net
          )
  
      model.AddParameter(weight, ParameterTags.WEIGHT)
      model.AddParameter(bias, ParameterTags.BIAS)
      return op_call([blob_in, weight, bias], blob_out, **kwargs)
  
label_desc: Use image node_id 1 for calling a global function with example usage: _elementwise_linear(model, op_call, blob_in, blob_out, dim, weight_init, bias_init) and returns: op_call