class_docstr: Apply transformations to the model.
  
  Transforms the original model to perform better while quantized
  and also match the layout of the target backend.
docstr: Transform model to a quantization friendly model.
  
  Args:
    model: Keras model to be quantized.
    layer_quantize_map: Map containing list of layers to be quantized and
      associated metadata. Keys are layer names which need to be quantized,
      and values are dicts containing relevant metadata. For example,
      any custom `QuantizeConfig` passed with a layer is present.
  
  Returns:
    New keras model based on `model` which has been
    transformed to match the layout of the target backend.
docstr_tok: ["Transform","model","to","a","quantization","friendly","model",".","Args",":","model",":","Keras","model","to","be","quantized",".","layer_quantize_map",":","Map","containing","list","of","layers","to","be","quantized","and","associated","metadata",".","Keys","are","layer","names","which","need","to","be","quantized",",","and","values","are","dicts","containing","relevant","metadata",".","For","example",",","any","custom","`","QuantizeConfig","`","passed","with","a","layer","is","present",".","Returns",":","New","keras","model","based","on","`","model","`","which","has","been","transformed","to","match","the","layout","of","the","target","backend","."]
code: def apply(self, model, layer_quantize_map):
      """Transform model to a quantization friendly model.
  
      Args:
        model: Keras model to be quantized.
        layer_quantize_map: Map containing list of layers to be quantized and
          associated metadata. Keys are layer names which need to be quantized,
          and values are dicts containing relevant metadata. For example,
          any custom `QuantizeConfig` passed with a layer is present.
  
      Returns:
        New keras model based on `model` which has been
        transformed to match the layout of the target backend.
      """
      raise NotImplementedError("Must be implemented in subclasses.")
  
file_path: model-optimization/tensorflow_model_optimization/python/core/quantization/keras/quantize_layout_transform.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import abc
  import six
label_desc: Use image node_id 1 for calling the QuantizeLayoutTransform obj's underlying member method code with example usage: obj.apply(model, layer_quantize_map) without return types