func getRandomRange(min, max int, seed int64) int {
	// special value -1 means no explicit seeding.
	if seed != -1 {
		rand.Seed(seed)
	}
	return rand.Intn(max-min) + min
}

// Randomizes the order of bytes in the byte array
// using Knuth Fisher-Yates shuffle algorithm.
func randomizeBytes(s []byte, seed int64) []byte {
	// special value -1 means no explicit seeding.

   */
  // Define
  TF_Operation* feed = Placeholder(func_graph_, s_);
  Define(-1, {}, {feed}, {feed}, {});

  // Use, run, and verify
  TF_Operation* func_feed = Placeholder(host_graph_, s_);
  TF_Operation* func_op = Use({func_feed});
  Run({{func_feed, Int32Tensor(3)}}, func_op, 3);
  VerifyFDef(empty_, {{"feed_0", DT_INT32}}, {{"feed", DT_INT32}},
             {{"feed_0", "feed"}}, {});
}

  EXPECT_EQ(1, TF_OperationOutputListLength(feed, "output", s));
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
  EXPECT_EQ(0, TF_OperationNumInputs(feed));
  EXPECT_EQ(0, TF_OperationOutputNumConsumers(TF_Output{feed, 0}));
  EXPECT_EQ(0, TF_OperationNumControlInputs(feed));
  EXPECT_EQ(0, TF_OperationNumControlOutputs(feed));

  tensorflow::AttrValue attr_value;

    environ_cp['TF_NEED_CUDA'] = '0'
  else:
    environ_cp['TF_NEED_CUDA'] = str(
        int(get_var(environ_cp, 'TF_NEED_CUDA', 'CUDA', False)))
  if (environ_cp.get('TF_NEED_CUDA') == '1' and
      'TF_CUDA_CONFIG_REPO' not in environ_cp):

    set_action_env_var(
        environ_cp,
        'TF_NEED_TENSORRT',
        'TensorRT',
        False,
        bazel_config_name='tensorrt')

        ),
    ] = None,
    include_in_schema: Annotated[
        bool,
        Doc(
            """
            To include (or not) this parameter field in the generated OpenAPI.
            You probably don't need it, but it's available.

            This affects the generated OpenAPI (e.g. visible at `/docs`).
            """
        ),
    ] = True,
    json_schema_extra: Annotated[
        Union[Dict[str, Any], None],

				// Linux returns InvalidArg for directory O_DIRECT
				// we need to keep this fallback code to return correct
				// errors upwards.
				return nil, dmTime, errFileNotFound
			}
			return nil, dmTime, errUnsupportedDisk
		}
		return nil, dmTime, err
	}

	if discard {
		// This discard is mostly true for DELETEEs
		// so we need to make sure we do not keep
		// page-cache references after.

		//   into memory, any mutation on xl.meta subsequently is
		//   inconsequential to the overall read operation.
		// - xl.meta metadata is still verified for quorum under lock()
		//   however writing the response doesn't need to serialize
		//   concurrent writers
		unlockOnDefer = true
		nsUnlocker = func() { lock.RUnlock(lkctx) }
	}

	fi, metaArr, onlineDisks, err := er.getObjectFileInfo(ctx, bucket, object, opts, true)

build:tpu --copt=-DLIBTPU_ON_GCE
build:tpu --define=enable_mlir_bridge=true

build:tensorrt --repo_env TF_NEED_TENSORRT=1

build:rocm --crosstool_top=@local_config_rocm//crosstool:toolchain
build:rocm --define=using_rocm_hipcc=true
build:rocm --define=tensorflow_mkldnn_contraction_kernel=0
build:rocm --repo_env TF_NEED_ROCM=1

build:sycl --crosstool_top=@local_config_sycl//crosstool:toolchain
build:sycl --define=using_sycl=true

   * is {@code 60}, and the concurrency level is {@code 8}, then eight segments are created, each
   * having a hash table of size eight. Providing a large enough estimate at construction time
   * avoids the need for expensive resizing operations later, but setting this value unnecessarily
   * high wastes memory.
   *
   * @return this {@code CacheBuilder} instance (for chaining)

//
// Note that to test the local filesystem we use an empty value.

namespace tensorflow {
namespace {

using ::tensorflow::error::Code;

// As we need to test multiple URI schemes we need a parameterized test.
// Furthermore, since each test creates and deletes files, we will use the same
// fixture to create new directories in `SetUp`. Each directory will reside in