code: def convert_sequential_model(
      model_config,
      nonlinear_mxts_mode=NonlinearMxtsMode.DeepLIFT_GenomicsDefault,
      verbose=True,
      dense_mxts_mode=DenseMxtsMode.Linear,
      conv_mxts_mode=ConvMxtsMode.Linear,
      maxpool_deeplift_mode=default_maxpool_deeplift_mode,
      layer_overrides={},
      custom_conversion_funcs={},
  ):
      if verbose:
          print(
              "nonlinear_mxts_mode is set to: "
              + str(nonlinear_mxts_mode)
          )
          sys.stdout.flush()
  
      converted_layers = []
      batch_input_shape = model_config[0]["config"][
          KerasKeys.batch_input_shape
      ]
      converted_layers.append(
          layers.core.Input(batch_shape=batch_input_shape, name="input")
      )
      # converted_layers is actually mutated to be extended with the
      # additional layers so the assignment is not strictly necessary,
      # but whatever
      converted_layers = sequential_container_conversion(
          config=model_config,
          name="",
          verbose=verbose,
          nonlinear_mxts_mode=nonlinear_mxts_mode,
          dense_mxts_mode=dense_mxts_mode,
          conv_mxts_mode=conv_mxts_mode,
          maxpool_deeplift_mode=maxpool_deeplift_mode,
          converted_layers=converted_layers,
          layer_overrides=layer_overrides,
      )
      converted_layers[-1].build_fwd_pass_vars()
      return models.SequentialModel(converted_layers)
  
imports: from __future__ import division
  from __future__ import print_function
  from __future__ import absolute_import
  import sys
  import os
  from collections import OrderedDict
  import deeplift
  from deeplift import models, layers
  from deeplift.layers.core import NonlinearMxtsMode, DenseMxtsMode, ConvMxtsMode, MaxPoolDeepLiftMode
  import deeplift.util
  import numpy
  import json
  import yaml
  import h5py
label_desc: Use image node_id 29 for calling a global function with example usage: convert_sequential_model(model_config, nonlinear_mxts_mode, verbose, dense_mxts_mode, conv_mxts_mode, maxpool_deeplift_mode, layer_overrides, custom_conversion_funcs) and returns: models