* equals</i>. That is, {@code a.compareTo(b)} or {@code comparator.compare(a, b)} should equal zero
 * <i>if and only if</i> {@code a.equals(b)}. If this advice is not followed, the resulting map will
 * not correctly obey its specification.
 *
 * <p>See the Guava User Guide article on <a href=

   *   Iterator<E> it = set2.descendingIterator();
   *   while (it.hasNext())
   *     foo(it.next());
   *   }
   * }
   * }</pre>
   *
   * <p>Failure to follow this advice may result in non-deterministic behavior.
   *
   * <p>The returned navigable set will be serializable if the specified navigable set is
   * serializable.
   *

		// not collecting memory because some non-pointer block of memory
		// had a bit pattern that matched a memory address.
		//
		// However, on arm64, we ignore all this advice above and slam the
		// allocation at 0x40 << 32 because when using 4k pages with 3-level
		// translation buffers, the user address space is limited to 39 bits
		// On ios/arm64, the address space is even smaller.
		//

   *   Iterator<Long> it = set.iterator(); // Must be in synchronized block
   *   while (it.hasNext()) {
   *     foo(it.next());
   *   }
   * }
   * }</pre>
   *
   * <p>Failure to follow this advice may result in non-deterministic behavior.
   *
   * <p>The returned bimap will be serializable if the specified bimap is serializable.
   *
   * @param bimap the bimap to be wrapped in a synchronized view

   *   Iterator<V> i = values.iterator(); // Must be in synchronized block
   *   while (i.hasNext()) {
   *     foo(i.next());
   *   }
   * }
   * }</pre>
   *
   * <p>Failure to follow this advice may result in non-deterministic behavior.
   *
   * <p>Note that the generated multimap's {@link Multimap#removeAll} and {@link
   * Multimap#replaceValues} methods return collections that aren't synchronized.
   *

		if ld.AllowErrors {
			// The user is explicitly ignoring these errors, so don't bother them with
			// other options.
			return
		}

		// We print directly to os.Stderr because this information is advice about
		// how to fix errors, not actually an error itself.
		// (The actual errors should have been logged already.)

		fmt.Fprintln(os.Stderr)

		goFlag := ""

    return std::nullopt;

  if (!HasValidHardwareTarget(op)) return std::nullopt;

  auto device = op->getAttrOfType<mlir::StringAttr>(mlir::TFL::tac::kDevice);
  if (device == nullptr) return std::nullopt;

  llvm::StringRef device_name_str = device.getValue();
  return device_name_str.str();
}

std::optional<std::vector<float>> GetPerDeviceCosts(

      task_and_device = {task, device};
    }
  }

  return topology;
}

// Determine execution devices when topology and device assignment are defined.
// With a topology device coordinate to task and device mapping, device
// assignment device coordinates can then be mapped to task and device for TPU
// devices. The device assignment array is also validated.
//

  %0 = "tf.Const"() {device = "/device:CPU:0", value = dense<2> : tensor<i32>} : () -> tensor<i32>
  %1 = "tf.ReadVariableOp"(%arg) {device = "/device:CPU:0", dtype = i32} : (tensor<!tf_type.resource<tensor<i32>>>) -> tensor<i32>
  %2 = "tf.Add"(%1, %0) {device = "/device:CPU:0"} : (tensor<i32>, tensor<i32>) -> tensor<i32>

"909", tf.device = "/job:tpu_host_worker/replica:0/task:0/device:CPU:0"}, %arg7: tensor<*x!tf_type.resource<tensor<25001x64xf32>>> {tf._user_specified_name = "911", tf.device = "/job:tpu_host_worker/replica:0/task:0/device:CPU:0"}, %arg8: tensor<*x!tf_type.resource<tensor<25001x64xf32>>> {tf._user_specified_name = "913", tf.device = "/job:tpu_host_worker/replica:0/task:1/device:CPU:0"}, %arg9: tensor<*x!tf_type.resource<tensor<25001x64xf32>>> {tf._user_specified_name = "915", tf.device = "/job:t...