continue
		}
		tokens = append(tokens, substitution...)
	}
	in.Push(NewSlice(in.Base(), in.Line(), tokens))
}

// argsFor returns a map from formal name to actual value for this argumented macro invocation.
// The opening parenthesis has been absorbed.
func (in *Input) argsFor(macro *Macro) map[string][]Token {
	var args [][]Token
	// One macro argument per iteration. Collect them all and check counts afterwards.

			encodingType: "gzip",
		},
	}

	for i, testCase := range testCases {
		prefix, marker, delimiter, maxKeys, encodingType, argsErr := getListObjectsV1Args(testCase.values)
		if argsErr != ErrNone {
			t.Errorf("Test %d: argument parsing failed, got %v", i+1, argsErr)
		}
		if prefix != testCase.prefix {
			t.Errorf("Test %d: Expected %s, got %s", i+1, testCase.prefix, prefix)
		}

pkg go/ast, type BasicLit struct, ValueEnd token.Pos #76031
pkg go/ast, type Directive struct #68021
pkg go/ast, type Directive struct, Args string #68021
pkg go/ast, type Directive struct, ArgsPos token.Pos #68021
pkg go/ast, type Directive struct, Name string #68021
pkg go/ast, type Directive struct, Slash token.Pos #68021
pkg go/ast, type Directive struct, Tool string #68021

    *   Add meta-distribution BatchReshape which reshapes batch dimensions.
    *   `tf.contrib.layers.recompute_grad` works for explicit gradient
        checkpointing on TPU.
    *   Add `tf.contrib.framework.argsort`.
    *   Allow `DNNBoostedTreeCombinedEstimator` to work with core versions of
        feature columns and losses.
    *   Add non-linear image warping ops: `tf.contrib.image.sparse_image_warp`,