import tensorflow as tf

input_dict = {
    'diagonal': tf.constant([1,2], dtype=tf.int32),
    'k': tf.constant([1, 2], dtype=tf.int32)
}

# crash
tf.experimental.numpy.diag(
    v=tf.constant([1,2], dtype=tf.int32), k=tf.constant([1, 2], dtype=tf.int32)
)

# crash
# tf.compat.v1.linalg.diag(
#     diagonal=tf.constant([1,2], dtype=tf.int32),
#     name='diag',
#     k= tf.constant([1, 2], dtype=tf.int32),
#     num_rows=-1,
#     num_cols=-1,
#     padding_value=0,
#     align='RIGHT_LEFT'
# )

# crash
# tf.experimental.numpy.diagflat(
#     v=tf.constant([1,2], dtype=tf.int32), k=tf.constant([1, 2], dtype=tf.int32)
# )

# crash
# tf.keras.ops.diag(
#     x=tf.constant([1,2], dtype=tf.int32), k=tf.constant([1, 2], dtype=tf.int32)
# )

2024-06-10 13:23:09.021306: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-06-10 13:23:10.029041: F tensorflow/core/framework/tensor_shape.cc:356] Check failed: d >= 0 (0 vs. -1)
Aborted (core dumped)