code: def generate_tf_onnx_model_multiple_inputs_float32():
      graph = tf.Graph()
      with graph.as_default():
          t_in1 = tf.placeholder(tf.float32, 10, name="first_input")
          t_in2 = tf.placeholder(tf.float32, 10, name="second_input")
          t_out = tf.multiply(t_in1, t_in2)
          tf.identity(t_out, name="output")
  
      sess = tf.Session(graph=graph)
  
      onnx_graph = tf2onnx.tfonnx.process_tf_graph(
          sess.graph,
          input_names=["first_input:0", "second_input:0"],
          output_names=["output:0"],
      )
      model_proto = onnx_graph.make_model("test")
  
      onnx.save_model(
          model_proto, "tf_model_multiple_inputs_float32.onnx"
      )
  
file_path: mlflow/tests/resources/onnx/generate_onnx_models.py
imports: import numpy
  import onnx
  import onnxmltools
  import pandas
  import tensorflow.compat.v1
  import tf2onnx
  from skl2onnx.common.data_types import FloatTensorType
  from sklearn import datasets
  from sklearn.linear_model import LogisticRegression
label_desc: Use image node_id 2 for calling a global function with example usage: generate_tf_onnx_model_multiple_inputs_float32() without return types