s := syscall.Statfs_t{}
	err = syscall.Statfs(path, &s)
	if err != nil {
		return Info{}, err
	}
	reservedBlocks := s.Bfree - s.Bavail
	info = Info{
		Total:  uint64(s.Bsize) * (s.Blocks - reservedBlocks),
		Free:   uint64(s.Bsize) * s.Bavail,
		Files:  s.Files,
		Ffree:  s.Ffree,
		FSType: getFSType(s.Fstypename[:]),
	}
	if info.Free > info.Total {

    outputs=['o: T'])
def _composite_max_pool(input_, stride_w, stride_h, filter_width, filter_height,
                        padding):
  ksize = [1, filter_width, filter_height, 1]
  strides = [1, stride_w, stride_h, 1]
  return tf.raw_ops.MaxPool(
      input=input_, ksize=ksize, strides=strides, padding=padding)


@tf.RegisterGradient('NewMaxPool')
def _max_pool_grad(op: ops.Operation, grad):

		t.Fatalf("could not read test data: %v", err)
	}

	d := &Data{}
	d.order = binary.LittleEndian
	if err := d.AddSection(".debug_rnglists", rngLists); err != nil {
		t.Fatal(err)
	}
	u := &unit{
		asize: 8,
		vers:  5,
		is64:  true,
	}
	ret, err := d.dwarf5Ranges(u, nil, 0x5fbd, 0xc, [][2]uint64{})
	if err != nil {
		t.Fatalf("could not read rnglist: %v", err)
	}
	t.Logf("%#v", ret)

  // CHECK: %[[R1:.*]] = "tf.MaxPool"(%[[R0]]) <{data_format = "NHWC", explicit_paddings = [], ksize = [1, 3, 3, 1], padding = "SAME", strides = [1, 2, 2, 1]}>
  // CHECK: %[[CST_0:.*]] = "tf.Const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi64>}>
  // CHECK: "tf.Transpose"(%[[R1]], %[[CST_0]])
  %0 = "tf.MaxPool"(%arg0)
       {
         data_format = "NCHW",
         ksize = [1, 1, 3, 3],
         padding = "SAME",
         strides = [1, 1, 2, 2]

import (
	"golang.org/x/sys/unix"
)

func getExpectedBlockSize(path string) int64 {
	statfs := &unix.Statfs_t{}
	err := unix.Statfs(path, statfs)
	if err != nil {
		return 0
	}

	return int64(statfs.Bsize)

  %3 = "tf.TensorSliceDataset"(%2) {device = "", output_shapes = [#tf_type.shape<>], metadata = ""} : (tensor<3xi32>) -> tensor<*x!tf_type.variant>
  // CHECK: "tf.MapAndBatchDataset"(%[[TSLICE]], %[[BSIZE:.*]], %[[NPC]]
  // CHECK-SAME: f = @"__inference_Dataset_map_<lambda>_80",
  %4 = "tf.MapDataset"(%3) {device = "",
           f = @"__inference_Dataset_map_<lambda>_80",

    %0:2 = tf_executor.graph {
      %outputs:2, %control = tf_executor.island wraps "tf.MaxPoolWithArgmax"(%arg0) {T = f32, Targmax = i32, include_batch_in_index = false, ksize = [1, 2, 2, 1], padding = "SAME", strides = [1, 2, 2, 1]} : (tensor<2x4x2x2xf32>) -> (tensor<2x2x1x2xf32>, tensor<2x2x1x2xi32>)
      tf_executor.fetch %outputs#1, %outputs#0 : tensor<2x2x1x2xi32>, tensor<2x2x1x2xf32>
    }

  %8 = "tf.MaxPool"(%7)
       {
         data_format = "NCHW",
         ksize = [1, 1, 3, 3],
         padding = "SAME",
         strides = [1, 1, 2, 2]
       } : (tensor<?x64x112x112xf32>) -> tensor<?x64x56x56xf32>

  // CHECK: %[[MAX_POOL:[0-9]*]] = "tf.MaxPool"
  // CHECK-SAME: data_format = "NHWC"
  // CHECK-SAME: ksize = [1, 3, 3, 1]
  // CHECK-SAME: strides = [1, 2, 2, 1]

    %1 = "quantfork.dcast"(%0) : (tensor<*x!quant.uniform<i8:f32, 0.05:-10>>) -> tensor<*xf32>
    %2 = "tf.MaxPool"(%1) {
      data_format = "NHWC", device = "", explicit_paddings = [],
      ksize = [1, 2, 2, 1], padding = "VALID", strides = [1, 2, 2, 1]
    } : (tensor<*xf32>) -> tensor<*xf32>
    %3 = "tf.Reshape"(%2, %cst) {device = ""} : (tensor<*xf32>, tensor<2xi32>) -> tensor<*xf32>

  %1 = "quantfork.dcast"(%0) : (tensor<*x!quant.uniform<i8:f32, 5.000000e-02:-10>>) -> tensor<*xf32>
  %2 = "tf.MaxPool"(%1) {data_format = "NHWC", device = "", explicit_paddings = [], ksize = [1, 2, 2, 1], padding = "VALID", strides = [1, 2, 2, 1]} : (tensor<*xf32>) -> tensor<*xf32>
  %3 = "quantfork.qcast"(%2) {volatile} : (tensor<*xf32>) -> tensor<*x!quant.uniform<i8:f32, 5.000000e-02:-10>>