code: def get_model(url, local_file, module):
      def get_tensor_type_str(tensor_type):
          """Get tensor type string representation when given TFLite tensor type"""
          try:
              from tflite.TensorType import TensorType
          except ImportError:
              raise ImportError("The tflite package must be installed")
  
          if tensor_type == TensorType.INT8:
              return "int8"
          if tensor_type == TensorType.INT16:
              return "int16"
          if tensor_type == TensorType.UINT8:
              return "uint8"
          if tensor_type == TensorType.FLOAT16:
              return "float16"
          if tensor_type == TensorType.FLOAT32:
              return "float32"
          if tensor_type == TensorType.INT32:
              return "int32"
          if tensor_type == TensorType.INT64:
              return "int64"
          if tensor_type == TensorType.BOOL:
              return "bool"
          raise NotImplementedError(
              "Tensor type {} is currently not supported".format(
                  str(tensor_type)
              )
          )
  
      if url is None:
          model_path = local_file
      else:
          model_path = tvm.contrib.download.download_testdata(
              url, local_file, module=module
          )
  
      with open(model_path, "rb") as f:
          tflite_model_buf = f.read()
  
      try:
          import tflite.Model
  
          tflite_model = tflite.Model.Model.GetRootAsModel(
              tflite_model_buf, 0
          )
      except AttributeError:
          import tflite
  
          tflite_model = tflite.Model.GetRootAsModel(
              tflite_model_buf, 0
          )
      except ImportError:
          raise ImportError("The tflite package must be installed")
  
      # keep the same as tflite
      assert (
          tflite_model.SubgraphsLength() == 1
      ), "only support one subgraph (main subgraph)"
      subgraph = tflite_model.Subgraphs(0)
  
      # model inputs
      model_inputs = subgraph.InputsAsNumpy()
      shape_dict = {}
      dtype_dict = {}
      for model_input in model_inputs:
          model_input_name = (
              subgraph.Tensors(model_input).Name().decode("utf-8")
          )
          model_shape_length = subgraph.Tensors(
              model_input
          ).ShapeLength()
          model_input_shape = [
              subgraph.Tensors(model_input).Shape(i)
              for i in range(model_shape_length)
          ]
          shape_dict[model_input_name] = model_input_shape
          dtype_dict[model_input_name] = get_tensor_type_str(
              subgraph.Tensors(model_input).Type()
          )
  
      # model Outputs
      model_outputs = subgraph.OutputsAsNumpy()
      shape_dict_out = {}
      dtype_dict_out = {}
      for model_output in model_outputs:
          model_output_name = (
              subgraph.Tensors(model_output).Name().decode("utf-8")
          )
          model_shape_length = subgraph.Tensors(
              model_output
          ).ShapeLength()
          model_output_shape = [
              subgraph.Tensors(model_output).Shape(i)
              for i in range(model_shape_length)
          ]
          shape_dict_out[model_output_name] = model_output_shape
          dtype_dict_out[model_output_name] = get_tensor_type_str(
              subgraph.Tensors(model_input).Type()
          )
  
      mod, params = relay.frontend.from_tflite(
          tflite_model, shape_dict=shape_dict, dtype_dict=dtype_dict
      )
  
      layout_config = relay.transform.LayoutConfig(skip_layers=[])
      desired_layouts = {"nn.conv2d": ["NCHW", "default"]}
      seq = tvm.transform.Sequential(
          [relay.transform.ConvertLayout(desired_layouts)]
      )
      with tvm.transform.PassContext(opt_level=3):
          mod = seq(mod)
  
      return mod, params, shape_dict, dtype_dict
  
file_path: tvm/tests/python/relay/opencl_texture/utils/adreno_utils.py
imports: import os
  import tvm
  import numpy
  from tvm import relay
  from tvm import autotvm
  from tvm import rpc
  from tvm.contrib import utils, ndk
  from tvm.relay import testing
  from tvm.relay.transform import recast
  from tvm.contrib import graph_runtime
  from tvm.runtime.vm import VirtualMachine
  import json
label_desc: Use image node_id 5 for calling a global function with example usage: get_model(url, local_file, module) and returns: mod,  params,  shape_dict,  dtype_dict, str, str, str, str, str, str, str, str