for dtype in dtype_list:
  lhs = tf.constant([-1, -5, -3, -14], dtype=dtype)
  rhs = tf.constant([5, 0, 7, 11], dtype=dtype)

  right_shift_result = bitwise_ops.right_shift(lhs, rhs)

  print(right_shift_result)

# This will print:
# tf.Tensor([-1 -5 -1 -1], shape=(4,), dtype=int8)
# tf.Tensor([-1 -5 -1 -1], shape=(4,), dtype=int16)

meta_graphs {
  meta_info_def {
    stripped_op_list {
      op {
        name: "Const"
        output_arg {
          name: "output"
          type_attr: "dtype"
        }
        attr {
          name: "value"
          type: "tensor"
        }
        attr {
          name: "dtype"
          type: "type"
        }
      }
      op {
        name: "Identity"
        input_arg {
          name: "input"

						CephMonitors: []string{"foo"},
						RBDImage:     "bar",
						FSType:       "ext4",
					},
				},
			},
		}, {
			name: "empty rbd monitors",
			vol: core.Volume{
				Name: "rbd",
				VolumeSource: core.VolumeSource{
					RBD: &core.RBDVolumeSource{
						CephMonitors: []string{},
						RBDImage:     "bar",
						FSType:       "ext4",
					},
				},
			},
			errs: []verr{{

        "properties": {
          "fsType": {

        the layer's dtype. If you had a model that used `float64`, it will
        probably silently use `float32` in TensorFlow2, and a warning will be
        issued that starts with Layer "layer-name" is casting an input tensor
        from dtype float64 to the layer's dtype of float32. To fix, either set
        the default dtype to float64 with

- Adjusts the fsType for cinder values to be `ext4` if no fsType is specified. ([#90608](https://github.com/kubernetes/kubernetes/pull/90608), [@huffmanca](https://github.com/huffmanca)) [SIG Storage]