core.VolumeMount{Name: "foo", MountPath: "/foo"},
		defaultContainer,
		false,
	}, {
		// implicitly non-privileged container + HostToContainer
		core.VolumeMount{Name: "foo", MountPath: "/foo", MountPropagation: &propagationHostToContainer},
		defaultContainer,
		false,
	}, {
		// non-privileged container + None
		core.VolumeMount{Name: "foo", MountPath: "/foo", MountPropagation: &propagationNone},

                                            /foo/        | /foo         | /xyz           | /xyz/
                                            /foo/bar     | /foo         | <empty string> | /bar
                                            /foo/        | /foo         | <empty string> | /
                                            /foo         | /foo         | <empty string> | /

  `(3,)` tensor `foo[:]`, the effective `begin` and `end` are `0` and `3`. Do
  not assume this is equivalent to `foo[0:-1]` which has an effective `begin`
  and `end` of `0` and `2`. Another example is `foo[-2::-1]` which reverses the
  first dimension of a tensor while dropping the last two (in the original
  order elements). For example `foo = [1,2,3,4]; foo[-2::-1]` is `[4,3]`.

            "type": "string"
          },
          "kind": {
            "default": "",
            "description": "kind is the kind for the resource (e.g. 'Foo' is the kind for a resource 'foo')",
            "type": "string"
          },
          "name": {
            "default": "",
            "description": "name is the plural name of the resource.",
            "type": "string"

            "type": "string"
          },
          "kind": {
            "default": "",
            "description": "kind is the kind for the resource (e.g. 'Foo' is the kind for a resource 'foo')",
            "type": "string"
          },
          "name": {
            "default": "",
            "description": "name is the plural name of the resource.",
            "type": "string"

*   Improved support for NumPy style basic slicing including non-1 strides,
    ellipses, newaxis, and negative indices. For example complicated expressions
    like `foo[1, 2:4, tf.newaxis, ..., :-3:-1, :]` are now supported. In
    addition we have preliminary (non-broadcasting) support for sliced
    assignment to variables. In particular one can write
    `var[1:3].assign([1,11,111])`.