docstr: Returns
  -------
          : dict
          Containing model weights and shapes
          {'data': weight data dict, 'shapes': weight shapes dict}
          Model is saved in CoreML format, hence dense weights and
          shapes are transposed.
docstr_tok: ["Returns","--","--","--","-",":","dict","Containing","model","weights","and","shapes","{","'data","'",":","weight","data","dict",",","'shapes","'",":","weight","shapes","dict","}","Model","is","saved","in","CoreML","format",",","hence","dense","weights","and","shapes","are","transposed","."]
code: def get_weights(self):
      """
      Returns
      -------
              : dict
              Containing model weights and shapes
              {'data': weight data dict, 'shapes': weight shapes dict}
              Model is saved in CoreML format, hence dense weights and
              shapes are transposed.
  
      """
      assert self.is_initialized
      _tf = _lazy_import_tensorflow()
  
      with self.sc_graph.as_default():
          layer_names = _tf.trainable_variables()
          layer_weights = self.sess.run(layer_names)
  
      data = {}
      shapes = {}
      for var, val in zip(layer_names, layer_weights):
          layer_name = var.name[:-2]
          if "bias" in layer_name:
              data[layer_name] = val
          else:
              data[layer_name] = val.transpose(1, 0)
          shapes[layer_name] = val.shape[::-1]
  
      return {"data": data, "shapes": shapes}
  
file_path: turicreate/src/python/turicreate/toolkits/sound_classifier/_tf_sound_classifier.py
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  from .._tf_model import TensorFlowModel
  import turicreate.toolkits._tf_utils
  from turicreate._deps.minimal_package import _minimal_package_import_check
label_desc: Use image node_id 10 for calling the SoundClassifierTensorFlowModel obj's underlying member method code with example usage: obj.get_weights() and returns: dict