}

// DeleteObjects deletes objects/versions in bulk, this function will still automatically split objects list
// into smaller bulks if some object names are found to be duplicated in the delete list, splitting
// into smaller bulks will avoid holding twice the write lock of the duplicated object names.

		t.Fatalf("open %q failed: %v", dir, err2)
	}
	defer file1.Close()
	small := smallReaddirnames(file1, len(all)+100, t) // +100 in case we screw up
	if len(small) < len(all) {
		t.Fatalf("len(small) is %d, less than %d", len(small), len(all))
	}
	for i, n := range all {
		if small[i] != n {
			t.Errorf("small read %q mismatch: %v", small[i], n)
		}
	}
}

func TestReaddirNValues(t *testing.T) {

  // Timed Get
  // There are a few design constraints to consider
  // * We want to be responsive to small timeouts, unpark() has non trivial latency overheads (I
  //   have observed 12 micros on 64-bit linux systems to wake up a parked thread). So if the
  //   timeout is small we shouldn't park(). This needs to be traded off with the cpu overhead of

  // Timed Get
  // There are a few design constraints to consider
  // * We want to be responsive to small timeouts, unpark() has non trivial latency overheads (I
  //   have observed 12 micros on 64-bit linux systems to wake up a parked thread). So if the
  //   timeout is small we shouldn't park(). This needs to be traded off with the cpu overhead of

//
// There are several steps between the time that an M experiences contention and
// when that contention may be added to the profile. This comes from our
// constraints: We need to keep the critical section of each lock small,
// especially when those locks are contended. The reporting code cannot acquire
// new locks until the M has released all other locks, which means no memory
// allocations and encourages use of (temporary) M-local storage.
//

          all_valid = False
        else:
          ver = int(sm_compute_match.group(2))
          if ver < 30:
            print(
                'ERROR: TensorFlow only supports small CUDA compute'
                ' capabilities of sm_30 and higher. Please re-specify the list'
                ' of compute capabilities excluding version %s.' % ver)
            all_valid = False
          if ver < 35:

		state.currentState.registers = make([][]SlotID, len(state.registers))
	} else {
		state.currentState.registers = state.currentState.registers[:len(state.registers)]
	}

	// A relatively small slice, but used many times as the return from processValue.
	state.changedVars = newSparseSet(numVars)
	state.changedSlots = newSparseSet(numSlots)

	}

	if a > elfreserve {
		Errorf(nil, "ELFRESERVE too small: %d > %d with %d text sections", a, elfreserve, numtext)
	}

	// Verify the amount of space allocated for the elf header is sufficient.  The file offsets are
	// already computed in layout, so we could spill into another section.
	if a > int64(HEADR) {
		Errorf(nil, "HEADR too small: %d > %d with %d text sections", a, HEADR, numtext)
	}
}

//
// To avoid requesting more OS memory while there are unswept spans, when a
// goroutine needs another span, it first attempts to reclaim that much memory
// by sweeping. When a goroutine needs to allocate a new small-object span, it
// sweeps small-object spans for the same object size until it frees at least
// one object. When a goroutine needs to allocate large-object span from heap,
// it sweeps spans until it frees at least that many pages into heap. There is

//
// The above copyright notice and this permission notice shall be included in
// all copies or substantial portions of the Software.
//
// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER