code: def _install_test_layer(op_type, parameters, weights, input_data):
      para_cls_id = "Cntk" + op_type + "Parameters"
      para_instance = eval(".".join(("cntkmodel", para_cls_id)))()
      for key, value in parameters.items():
          setattr(para_instance, key, value)
      layer_def = cntkmodel.CntkLayersDefinition()
      layer_def.parameters = para_instance
      layer_def.op_type = getattr(
          cntkmodel.CntkLayerType, utils.format.camel_to_snake(op_type)
      )
      layer_def.op_name = "_".join(("test", op_type))
      layer_def.parameter_tensor = []
      if weights is not None:
          for weight in weights:
              weight_tensor = cntkmodel.CntkTensorDefinition()
              weight_tensor.tensor = np.array(weight).shape
              weight_tensor.data = weight
              layer_def.parameter_tensor.append(weight_tensor)
      inputs_variable = []
      for input_tensor in input_data:
          inputs_variable.append(cntk.input(input_tensor.shape))
      return layer_def, inputs_variable
  
file_path: CNTK/bindings/python/cntk/contrib/crosstalkcaffe/tests/op2cntk_test.py
imports: import pytest
  import numpy
  import cntk
  from cntk.contrib.crosstalkcaffe import utils
  from cntk.contrib.crosstalkcaffe.unimodel import cntkmodel
  from cntk.contrib.crosstalkcaffe.unimodel.cntkinstance import ApiSetup
label_desc: Use image node_id 3 for calling a global function with example usage: _install_test_layer(op_type, parameters, weights, input_data) and returns: layer_def,  inputs_variable