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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 get_custom_getter(self):
 """Returns a custom getter that this class's methods must be called All methods of this class must be called under a variable scope that was passed this custom getter. Example: ```python with tf.variable_scope("cg", custom_getter=network.get_custom_getter()):
 # Call more methods of network here ``` Currently, this custom getter only does anything if self.use_tf_layers is True. In that case, it causes variables to be stored as dtype self.variable_type, then casted to the requested dtype, instead of directly storing the variable as the requested dtype. """


        def inner_custom_getter(getter, *args, **kwargs):
            if not self.use_tf_layers:
                return getter(*args, **kwargs)
            requested_dtype = kwargs["dtype"]
            if not (requested_dtype == tf.float32
                    and self.variable_dtype == tf.float16):
                kwargs["dtype"] = self.variable_dtype
            var = getter(*args, **kwargs)
            if var.dtype.base_dtype != requested_dtype:
                var = tf.cast(var, requested_dtype)
            return var

        return inner_custom_getter