code: def testJaxArrayToTorch(self, shape, dtype):
      if xla_bridge.using_pjrt_c_api():
          self.skipTest(
              "DLPack support is incomplete in the PJRT C API"
          )
      if not config.enable_x64.value and dtype in [
          jnp.int64,
          jnp.float64,
          jnp.complex128,
      ]:
          self.skipTest("x64 types are disabled by jax_enable_x64")
      rng = jtu.rand_default(self.rng())
      np = rng(shape, dtype)
      # Test across all devices
      for device in jax.local_devices():
          x = jax.device_put(np, device)
          y = torch.utils.dlpack.from_dlpack(x)
          if dtype == jnp.bfloat16:
              # .numpy() doesn't work on Torch bfloat16 tensors.
              self.assertAllClose(
                  np,
                  y.cpu().view(torch.int16).numpy().view(jnp.bfloat16),
              )
          else:
              self.assertAllClose(np, y.cpu().numpy())
  
file_path: jax/tests/pytorch_interoperability_test.py
imports: import unittest
  from absl.testing import absltest
  import jax
  import jax.dlpack
  from jax._src import config
  from jax._src import test_util
  from jax._src import xla_bridge
  from jax._src.lib import xla_client
  import jax.numpy
label_desc: Use image node_id 4 for calling the DLPackTest obj's underlying member method code with example usage: obj.testJaxArrayToTorch(shape, dtype) without return types