code: def base_model(
      output_dir="hls4mlprj_graph_base_model", iotype="io_parallel"
  ):
      layers = [
          {
              "class_name": "Input",
              "name": "layer0_input",
              "input_shape": [1],
          },
          {
              "class_name": "Dense",
              "name": "layer0",
              "n_in": 1,
              "n_out": 1,
              "weight_data": w,
              "bias_data": b,
          },
          {
              "class_name": "Dense",
              "name": "layer1",
              "n_in": 1,
              "n_out": 1,
              "weight_data": w,
              "bias_data": b,
          },
      ]
      config = {
          "HLSConfig": {
              "Model": {
                  "Precision": "ap_fixed<32,16>",
                  "ReuseFactor": 1,
              },
              "Flows": [],
          }
      }
      config["OutputDir"] = output_dir
      config["ProjectName"] = "myprj"
      config["IOType"] = iotype
      config["Backend"] = "Vivado"
      model = hls4ml.model.ModelGraph(config, layers)
      return model
  
file_path: hls4ml/test/pytest/test_graph.py
imports: from pathlib import Path
  import numpy
  import pytest
  import tensorflow
  import hls4ml
label_desc: Use image node_id 1 for calling a global function with example usage: base_model(output_dir, iotype) and returns: model