code: def testTorchToJaxArray(self, shape, dtype):
      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())
      x_np = rng(shape, dtype)
      if dtype == jnp.bfloat16:
          x = torch.tensor(x_np.view(jnp.int16)).view(torch.bfloat16)
      else:
          x = torch.tensor(x_np)
      x = x.cuda() if jtu.test_device_matches(["gpu"]) else x
      x = x.contiguous()
      y = jax.dlpack.from_dlpack(x)
      self.assertAllClose(x_np, y)
  
      # Verify the resulting value can be passed to a jit computation.
      z = jax.jit(lambda x: x + 1)(y)
      self.assertAllClose(x_np + dtype(1), z)
  
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 7 for calling the DLPackTest obj's underlying member method code with example usage: obj.testTorchToJaxArray(shape, dtype) without return types