try ( SmbResource p = new SmbFile("smb://1.2.3.4/dfs/share/bar/", getContext()) ) {
            DfsReferralData dr = new TestDfsReferral("1.2.3.4", "target", "", 6); // consumes the /share dfs root path
            String reqPath = "\\share\\bar\\";
            SmbResourceLocator fl = p.getLocator();
            assertEquals("\\bar\\", ( (SmbResourceLocatorInternal) fl ).handleDFSReferral(dr, reqPath));

Either way, the outputs produced by any tasks depending on such data will only be re-usable by builds running against the exact same commit.

Another common, but less obvious source of unstable inputs is when a task consumes the output of another task which produces non-repeatable results, such as the example before of a code generator that embeds timestamps in its output.

A task can only be loaded from the cache if it has stable task inputs.

	line, column int    // line and column for current token; error position for invalid line directives
}

var segments = []segment{
	// exactly one token per line since the test consumes one token per segment
	{"  line1", "TestLineDirectives", 1, 3},
	{"\nline2", "TestLineDirectives", 2, 1},
	{"\nline3  //line File1.go:100", "TestLineDirectives", 3, 1}, // bad line comment, ignored

      tf_executor.NextIteration.sink [%token] (%value) : tensor<*xi32>

    Note: Additional result corresponds to the control output.
  }];

  let arguments = (ins
    // The NextIteration.Sink operation consumes the token produced by the source.
    TfeTokenType:$token,
    AnyType:$input,
    // Optional extra control inputs.
    Variadic<TfeControlType>:$controlInputs
  );

  let builders = [

		}
		panic(&SyntaxError{Offset: p.pos, Err: "unexpected token " + p.tok})
	}
	tok := p.tok
	p.lex()
	return tag(tok)
}

// lex finds and consumes the next token in the input stream.
// On return, p.tok is set to the token text,
// p.isTag reports whether the token was a tag,
// and p.pos records the byte offset of the start of the token in the input stream.

func (v indexType) indexed() bool   { return v == indexedTrue }
func (v indexType) sensitive() bool { return v == indexedNever }

// returns errNeedMore if there isn't enough data available.
// any other error is fatal.
// consumes d.buf iff it returns nil.
// precondition: must be called with len(d.buf) > 0
func (d *Decoder) parseHeaderFieldRepr() error {
	b := d.buf[0]
	switch {
	case b&128 != 0:
		// Indexed representation.

				# TYPE kubelet_working_pods gauge
				kubelet_working_pods{config="desired",lifecycle="sync",static=""} 0

`note`:`notesleep` also prevents rescheduling of any associated G and
P, while `notetsleepg` acts like a blocking system call that allows
the P to be reused to run another G. This is still less efficient than
blocking the G directly since it consumes an M.

To interact directly with the goroutine scheduler, use `gopark` and
`goready`. `gopark` parks the current goroutine—putting it in the
"waiting" state and removing it from the scheduler's run queue—and

And if your remote server or virtual machine only has 3 GB of RAM, trying to load more than 4 GB of RAM will cause problems. 🚨

### Multiple Processes - An Example

class VSpace {
 public:
  virtual ~VSpace() {}

  // Returns the number of elements in the gradient tensor.
  virtual int64_t NumElements(Gradient* tensor) const = 0;

  // Consumes references to the tensors in the gradient_tensors list and returns
  // a tensor with the result.
  virtual Gradient* AggregateGradients(
      gtl::ArraySlice<Gradient*> gradient_tensors) const = 0;