f2 := func() (x, y int64) { return }
	_ = unsafe.Sizeof(f0 /* ERROR "used as value" */ ())
	assert(unsafe.Sizeof(f1()) == 8)
	_ = unsafe.Sizeof(f2()) // ERROR "too many arguments"
}

func Slice1() {
	var x int
	unsafe.Slice()        // ERROR "not enough arguments"
	unsafe.Slice(1, 2, 3) // ERROR "too many arguments"
	unsafe.Slice(1 /* ERROR "is not a pointer" */ , 2)

  // CHECK: %[[SLICE_0:.*]] = "tfl.slice"(%arg0, %[[BEGIN_0]], %[[SHAPE_0]]) : (tensor<2x3x4x5xf32>, tensor<4xi64>, tensor<4xi64>) -> tensor<2x3x4x4xf32>
  // CHECK: %[[SLICE_1:.*]] = "tfl.slice"(%arg0, %[[BEGIN_1]], %[[SHAPE_1]]) : (tensor<2x3x4x5xf32>, tensor<4xi64>, tensor<4xi64>) -> tensor<1x2x3x4xf32>
  // CHECK: return %[[SLICE_0]], %[[SLICE_1]] : tensor<2x3x4x4xf32>, tensor<1x2x3x4xf32>
}

    results.reserve(op.getNumResults());

    for (int i = 0, end = op.getNumResults(); i < end; ++i) {
      begin_indices[axis] = i;
      end_indices[axis] = i + 1;

      auto slice_op = rewriter.create<mhlo::SliceOp>(
          op.getLoc(), op.getValue(),
          GetI64ElementsAttr(begin_indices, &rewriter),
          GetI64ElementsAttr(end_indices, &rewriter),

            tflite::BuiltinOptions2_StablehloConcatenateOptions,
            concat_option.Union());
      }
      if (auto vhlo_op = llvm::dyn_cast<mlir::vhlo::SliceOpV1>(inst)) {
        std::string op_name = inst->getName().getStringRef().str();
        uint32_t opcode_index =
            GetOpcodeIndex(op_name, tflite::BuiltinOperator_STABLEHLO_SLICE);

":"IAAfAIeJcncBjvcpYDdH/3BSJNFLGLJj/Pqd3hOZkzI9dFoHfQncV/Xw4Iuhw==","X-Minio-Internal-Server-Side-Encryption-Seal-Algorithm":"DAREv2-HMAC-SHA256","content-type":"application/octet-stream"}},{"Bucket":"buck1","Name":"go_113/src/cmd/gofmt/testdata/slices1.input","UserDef":{"X-Minio-Internal-Server-Side-Encryption-Iv":"pkH+u4J9RXoJNS9LAkC13a1a=","X-Minio-Internal-Server-Side-Encryption-S3-Kms-Key-Id":"my-minio-key","X-Minio-Internal-Server-Side-Encryption-S3-Kms-Sealed-Key":"IAAfAK8k/iUT97JRstdZF4v...

  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
}
node {
  name: "training/LossScaleOptimizer/gradients/pool1_pad_1/Pad_grad/Slice_1"
  op: "Slice"
  input: "training/LossScaleOptimizer/gradients/max_pooling2d_1/MaxPool_grad/MaxPoolGrad"
  input: "training/LossScaleOptimizer/gradients/pool1_pad_1/Pad_grad/Reshape"

   `dimI` are the dimensions of the larger tensor and `slice-spec`
   specifies what part is covered by the tensor to save.

`slice-spec` itself is a `:`-separated list: `slice0:slice1:...:sliceN-1`
where each `sliceI` is either:

*  The string `-` meaning that the slice covers all indices of this dimension
*  `start,length` where `start` and `length` are integers.  In that