GHSA-79h2-q768-fpxr

import tensorflow as tf class QuantConv2DTransposed(tf.keras.layers.Layer): def build(self, input_shape): self.kernel = self.add_weight("kernel", [3, 3, input_shape[-1], 24]) def call(self, inputs): filters = tf.quantization.fake_quant_with_min_max_vars_per_channel( self.kernel, -3.0 * tf.ones([24]), 3.0 * tf.ones([24]), narrow_range=True ) filters = tf.transpose(filters, (0, 1, 3, 2)) return tf.nn.conv2d_transpose(inputs, filters, [*inputs.shape[:-1], 24], 1) inp = tf.keras.Input(shape=(6, 8, 48), batch_size=1) x = tf.quantization.fake_quant_with_min_max_vars(inp, -3.0, 3.0, narrow_range=True) x = QuantConv2DTransposed()(x) x = tf.quantization.fake_quant_with_min_max_vars(x, -3.0, 3.0, narrow_range=True) model = tf.keras.Model(inp, x) model.save("/tmp/testing") converter = tf.lite.TFLiteConverter.from_saved_model("/tmp/testing") converter.optimizations = [tf.lite.Optimize.DEFAULT] # terminated by signal SIGSEGV (Address boundary error) tflite_model = converter.convert()