code: def validate(
      platform,
      model_file,
      weight_file,
      input_file,
      mace_out_file,
      device_type,
      input_shape,
      output_shape,
      input_data_format_str,
      output_data_format_str,
      input_node,
      output_node,
      validation_threshold,
      input_data_type,
      output_data_type,
      backend,
      validation_outputs_data,
      log_file,
  ):
      input_names = [name for name in input_node.split(",")]
      input_shape_strs = [shape for shape in input_shape.split(":")]
      input_shapes = [
          [int(x) for x in common.split_shape(shape)]
          for shape in input_shape_strs
      ]
      output_shape_strs = [shape for shape in output_shape.split(":")]
      output_shapes = [
          [int(x) for x in common.split_shape(shape)]
          for shape in output_shape_strs
      ]
      input_data_formats = [
          df for df in input_data_format_str.split(",")
      ]
      output_data_formats = [
          df for df in output_data_format_str.split(",")
      ]
      if input_data_type:
          input_data_types = [
              data_type for data_type in input_data_type.split(",")
          ]
      else:
          input_data_types = ["float32"] * len(input_names)
      if output_data_type:
          output_data_types = [
              data_type for data_type in output_data_type.split(",")
          ]
      else:
          output_data_types = ["float32"] * len(output_shapes)
      output_names = [name for name in output_node.split(",")]
      assert len(input_names) == len(input_shapes)
      if not isinstance(validation_outputs_data, list):
          if (
              os.path.isfile(validation_outputs_data)
              or validation_outputs_data.startswith("http://")
              or validation_outputs_data.startswith("https://")
          ):
              validation_outputs = [validation_outputs_data]
          else:
              validation_outputs = []
      else:
          validation_outputs = validation_outputs_data
      if validation_outputs:
          validate_with_file(
              platform,
              device_type,
              output_names,
              output_shapes,
              mace_out_file,
              validation_outputs,
              validation_threshold,
              log_file,
              output_data_formats,
          )
      elif platform == "tensorflow":
          validate_tf_model(
              platform,
              device_type,
              model_file,
              input_file,
              mace_out_file,
              input_names,
              input_shapes,
              input_data_formats,
              output_names,
              output_shapes,
              output_data_formats,
              validation_threshold,
              input_data_types,
              output_data_types,
              log_file,
          )
      elif platform == "pytorch":
          validate_pytorch_model(
              platform,
              device_type,
              model_file,
              input_file,
              mace_out_file,
              input_names,
              input_shapes,
              input_data_formats,
              output_names,
              output_shapes,
              output_data_formats,
              validation_threshold,
              input_data_types,
              output_data_types,
              log_file,
          )
      elif platform == "caffe":
          validate_caffe_model(
              platform,
              device_type,
              model_file,
              input_file,
              mace_out_file,
              weight_file,
              input_names,
              input_shapes,
              input_data_formats,
              output_names,
              output_shapes,
              output_data_formats,
              validation_threshold,
              log_file,
          )
      elif platform == "onnx":
          validate_onnx_model(
              platform,
              device_type,
              model_file,
              input_file,
              mace_out_file,
              input_names,
              input_shapes,
              input_data_formats,
              output_names,
              output_shapes,
              output_data_formats,
              validation_threshold,
              input_data_types,
              output_data_types,
              backend,
              log_file,
          )
      elif platform == "megengine":
          validate_megengine_model(
              platform,
              device_type,
              model_file,
              input_file,
              mace_out_file,
              input_names,
              input_shapes,
              input_data_formats,
              output_names,
              output_shapes,
              output_data_formats,
              validation_threshold,
              input_data_types,
              output_data_types,
              log_file,
          )
      elif platform == "keras":
          validate_keras_model(
              platform,
              device_type,
              model_file,
              input_file,
              mace_out_file,
              input_names,
              input_shapes,
              input_data_formats,
              output_names,
              output_shapes,
              output_data_formats,
              validation_threshold,
              input_data_types,
              output_data_types,
              log_file,
          )
  
imports: import argparse
  import sys
  import os
  import os.path
  import numpy
  import six
  import common
label_desc: Use image node_id 16 for calling a global function with example usage: validate(platform, model_file, weight_file, input_file, mace_out_file, device_type, input_shape, output_shape, input_data_format_str, output_data_format_str, input_node, output_node, validation_threshold, input_data_type, output_data_type, backend, validation_outputs_data, log_file) without return types