code: def prepare_request(inputs_meta, inputs_data, outputs_meta):
      """
      inputs_meta: inputs meta information from model. name: info
      inputs_data: users input data. name: data
      """
      # Set the input data
      inputs = []
      for input_dict in inputs_meta:
          input_name = input_dict["name"]
          if input_name not in inputs_data:
              raise RuntimeError(
                  "Error: input name {} required for model not existed.".format(
                      input_name
                  )
              )
          if input_dict["datatype"] == "FP32":
              inputs_data[input_name] = (
                  inputs_data[input_name].astype(np.float32) / 255
              )  # image data returned by gradio is uint8, convert to fp32
          if (
              len(input_dict["shape"]) == 3
              and input_dict["shape"][0] == 3
          ):  # NCHW
              inputs_data[input_name] = inputs_data[input_name][
                  0
              ].transpose(2, 0, 1)
          elif (
              len(input_dict["shape"]) == 4
              and input_dict["shape"][1] == 3
          ):  # NCHW
              inputs_data[input_name] = inputs_data[
                  input_name
              ].transpose(0, 3, 1, 2)
          infer_input = httpclient.InferInput(
              input_name,
              inputs_data[input_name].shape,
              input_dict["datatype"],
          )
          infer_input.set_data_from_numpy(inputs_data[input_name])
          inputs.append(infer_input)
      outputs = []
      for output_dict in outputs_meta:
          infer_output = httpclient.InferRequestedOutput(
              output_dict["name"]
          )
          outputs.append(infer_output)
      return inputs, outputs
  
file_path: VisualDL/visualdl/component/inference/fastdeploy_client/http_client_manager.py
imports: import json
  import re
  import numpy
  import requests
  import tritonclient.http
label_desc: Use image node_id 1 for calling a global function with example usage: prepare_request(inputs_meta, inputs_data, outputs_meta) and returns: inputs,  outputs