code: def test_causal_mask(self, make_causal_mask):
      expected = np.array([[1]], dtype=np.bool_)
      actual = make_causal_mask((1, 1))
  
      with self.subTest("unit"):
          self.assertArraysEqual(actual, expected)
  
      expected = np.array(
          [
              [1, 0, 0, 0],
              [1, 1, 0, 0],
              [1, 1, 1, 0],
              [1, 1, 1, 1],
          ],
          dtype=np.bool_,
      )
      actual = make_causal_mask((4, 4))
  
      with self.subTest("square"):
          self.assertArraysEqual(actual, expected)
  
      expected = np.array(
          [
              [1, 0, 0, 0, 0, 0],
              [1, 1, 0, 0, 0, 0],
              [1, 1, 1, 0, 0, 0],
              [1, 1, 1, 1, 0, 0],
          ],
          dtype=np.bool_,
      )
      actual = make_causal_mask((4, 6))
  
      with self.subTest("wide_rectangle"):
          self.assertArraysEqual(actual, expected)
  
      actual = make_causal_mask((6, 4))
      expected = np.array(
          [
              [1, 0, 0, 0],
              [1, 1, 0, 0],
              [1, 1, 1, 0],
              [1, 1, 1, 1],
              [1, 1, 1, 1],
              [1, 1, 1, 1],
          ],
          dtype=np.bool_,
      )
  
      with self.subTest("tall_rectangle"):
          self.assertArraysEqual(actual, expected)
  
      actual = make_causal_mask((4, 4), -1)
      expected = np.array(
          [
              [0, 0, 0, 0],
              [1, 0, 0, 0],
              [1, 1, 0, 0],
              [1, 1, 1, 0],
          ],
          dtype=np.bool_,
      )
  
      with self.subTest("negative_offset"):
          self.assertArraysEqual(actual, expected)
  
      actual = make_causal_mask((4, 4), 1)
      expected = np.array(
          [
              [1, 1, 0, 0],
              [1, 1, 1, 0],
              [1, 1, 1, 1],
              [1, 1, 1, 1],
          ],
          dtype=np.bool_,
      )
  
      with self.subTest("positive_offset"):
          self.assertArraysEqual(actual, expected)
  
imports: from __future__ import annotations
  from typing import List
  from absl.testing import absltest
  from absl.testing import parameterized
  import jax
  from jax.experimental.pallas.ops.tpu.splash_attention import splash_attention_mask
  from jax.experimental.pallas.ops.tpu.splash_attention import splash_attention_mask_info
  from jax._src import test_util
  import numpy
label_desc: Use image node_id 1 for calling the SplashAttentionMaskTest obj's underlying member method code with example usage: obj.test_causal_mask(make_causal_mask) without return types