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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def make_diet_var_getter(params):
 """Create a custom variable getter for diet variables according to params."""


  def diet_var_initializer(shape, dtype, partition_info=None):
    """Initializer for a diet variable."""
    del dtype
    del partition_info

    with common_layers.fn_device_dependency("diet_init") as out_deps:
      float_range = math.sqrt(3)
      ret = tf.random_uniform(shape, -float_range, float_range)
      if params.quantize:
        ret = _quantize(ret, params, randomize=False)
      out_deps.append(ret)
      return ret

  def diet_var_getter(getter, **kwargs):
    """Get diet variable and return it dequantized."""
    if params.quantize:
      kwargs["dtype"] = tf.float16
    kwargs["initializer"] = diet_var_initializer
    kwargs["trainable"] = False

    base_var = getter(**kwargs)

    dequantized = _dequantize(base_var, params)

    if not hasattr(params, "dequantized"):
      params.dequantized = defaultdict(list)
    params.dequantized[base_var.name].append(dequantized)

    return dequantized

  return diet_var_getter