TF_DeleteStatus(s);
    TF_DeleteTensor(output);
  };

  SetupOp("AllocateOutputOp1", "AllocateOutput1", my_compute_func);

  TF_ASSERT_OK(RunOpKernel());
  Tensor* output = GetOutput(0);
  EXPECT_EQ("Tensor<type: float shape: [1] values: 3>",
            output->DebugString(100));
}

REGISTER_OP("AllocateOutputOp0").Output("output1: float");

    TF::TPUReplicatedInputOp private_input = input.clone();
    builder.insert(private_input);
    forward_pass_ops.insert(private_input);

    input.getOutput().replaceUsesWithIf(
        private_input.getOutput(), [&](OpOperand& use) {
          return forward_pass_ops.contains(use.getOwner());
        });
  }

  VLOG(3) << "Cloned " << cloned_inputs << " TPUReplicatedInputOps";

        return false;
      }
      // Satisfies the input condition.
      has_quantized_types_for_weights = true;
    }
  }
  for (Value output : call_op.getOutput()) {
    if (auto type = mlir::dyn_cast<TensorType>(output.getType())) {
      if (mlir::isa<QuantizedType>(type.getElementType())) {
        return false;
      }
    }
  }

          rewriter.create<ConstOp>(op->getLoc(), scalar_type, dim_attr);
      auto concat_op = rewriter.create<ConcatV2Op>(
          op->getLoc(), input_ty,
          ArrayRef<Value>({slice_op_1.getOutput(), slice_op_2.getOutput()}),
          concat_dim);
      result = concat_op.getResult();
    }

    rewriter.replaceOp(op, result);
    return success();
  }
};

        DenseElementsAttr::get<int32_t>(strides_type, padded_strides));

    auto attribute_type = rewriter.getIntegerType(64);
    rewriter.replaceOpWithNewOp<TF::StridedSliceOp>(
        op, strided_slice_op.getOutput().getType(), strided_slice_op.getInput(),
        new_begin_attr, new_end_attr, new_strides_attr,
        rewriter.getIntegerAttr(attribute_type, begin_mask),
        rewriter.getIntegerAttr(attribute_type, end_mask),

        auto new_op = builder.create<tfl::QConstOp>(
            cst.getLoc(), new_output_type, mlir::TypeAttr::get(new_output_type),
            cst.getValueAttr());
        full_range_const = new_op.getOutput();
      }
      use.set(full_range_const);
    }
    if (cst.use_empty()) cst.erase();
  });
  return func;
}

// Helper method that returns the index of the tensor with name 'tensor_name'

	}
}

// Issue 18327: verify we always do a deep copy of the Request.Header map
// before any mutations.
func TestClonesRequestHeaders(t *testing.T) {
	log.SetOutput(io.Discard)
	defer log.SetOutput(os.Stderr)
	req, _ := http.NewRequest("GET", "http://foo.tld/", nil)
	req.RemoteAddr = "1.2.3.4:56789"
	rp := &ReverseProxy{
		Director: func(req *http.Request) {

			var buf bytes.Buffer
			klog.SetOutput(&buf)

			ctx := testContext(t)

			h := &kmsv2PluginProbe{
				name:    "panda",
				service: tt.service,
			}
			h.state.Store(&tt.state)

			err := h.rotateDEKOnKeyIDChange(ctx, tt.statusKeyID, "panda")

			klog.Flush()
			klog.SetOutput(io.Discard) // prevent further writes into buf

	options.AddKubeletConfigFlags(fs, kc)
	// Remember original feature gates, so we can merge with flag gates later
	original := kc.FeatureGates
	// avoid duplicate printing the flag deprecation warnings during re-parsing
	fs.SetOutput(io.Discard)
	// re-parse flags
	if err := fs.Parse(args); err != nil {
		return err
	}
	// Add back feature gates that were set in the original kc, but not in flags
	for k, v := range original {

	}
}

func ExampleQuantityValue() {
	q := QuantityValue{
		Quantity: MustParse("1Mi"),
	}
	fs := pflag.FlagSet{}
	fs.SetOutput(os.Stdout)
	fs.Var(&q, "mem", "sets amount of memory")
	fs.PrintDefaults()
	// Output:
	// --mem quantity   sets amount of memory (default 1Mi)
}

func TestQuantityUnmarshalCBOR(t *testing.T) {