code: def _make_model(
      input_name,
      input_length,
      output_name,
      output_length,
      convert_to="mlprogram",
      compute_units=ct.ComputeUnit.ALL,
  ):
      weight_tensor = np.arange(
          input_length * output_length, dtype="float32"
      )
      weight_tensor = weight_tensor.reshape(output_length, input_length)
  
      prog = Program()
      func_inputs = {input_name: mb.placeholder(shape=(input_length,))}
      with Function(func_inputs) as ssa_fun:
          input = ssa_fun.inputs[input_name]
          y = mb.linear(x=input, weight=weight_tensor, name=output_name)
          ssa_fun.set_outputs([y])
          prog.add_function("main", ssa_fun)
  
      return ct.convert(
          prog, convert_to=convert_to, compute_units=compute_units
      )
  
imports: import itertools
  import tempfile
  import unittest
  import numpy
  import pytest
  import coremltools
  from coremltools._deps import _HAS_LIBSVM, _HAS_SKLEARN
  from coremltools.converters.mil.mil import Builder
  from coremltools.converters.mil.mil import Function, Program
  from coremltools.models.pipeline import PipelineClassifier, PipelineRegressor
  from coremltools.models.utils import _is_macos
label_desc: Use image node_id 1 for calling the TestMakePipeline obj's underlying member method code with example usage: obj._make_model(input_name, input_length, output_name, output_length, convert_to, compute_units) and returns: ct