},
			},
		},
	}
	for _, tc := range cases {
		for _, op := range tc.opts {
			for _, typ := range tc.costs {
				t.Run(typ.comparableLiteral+op, func(t *testing.T) {
					e := typ.comparableLiteral + op
					testCost(t, e, typ.expectedEstimatedCost, typ.expectedRuntimeCost)
				})
			}
		}
	}
}

func TestIndexOfCost(t *testing.T) {

  }

  boolean isEmpty() {
    // The casts are safe because of the has*Bound() checks.
    return (hasUpperBound() && tooLow(uncheckedCastNullableTToT(getUpperEndpoint())))
        || (hasLowerBound() && tooHigh(uncheckedCastNullableTToT(getLowerEndpoint())));
  }

  boolean tooLow(@ParametricNullness T t) {
    if (!hasLowerBound()) {
      return false;
    }
    // The cast is safe because of the hasLowerBound() check.

  }

  boolean isEmpty() {
    // The casts are safe because of the has*Bound() checks.
    return (hasUpperBound() && tooLow(uncheckedCastNullableTToT(getUpperEndpoint())))
        || (hasLowerBound() && tooHigh(uncheckedCastNullableTToT(getLowerEndpoint())));
  }

  boolean tooLow(@ParametricNullness T t) {
    if (!hasLowerBound()) {
      return false;
    }
    // The cast is safe because of the hasLowerBound() check.

//===----------------------------------------------------------------------===//
// Op quantization pass-through patterns.
//===----------------------------------------------------------------------===//
// Casts result type of $1 to a quantized type by using the quantization
// parameters from the type in $0.
class UpdateShapeWithAxis<int i> : NativeCodeCall<

  pm.addNestedPass<mlir::func::FuncOp>(
      mlir::quant::CreateReplaceCastHacksWithTFXLAOpsPass());
  pm.addNestedPass<mlir::func::FuncOp>(mlir::createCSEPass());
  // Use optimize pass to remove double casts that are inserted when inlining
  // functions.
  pm.addNestedPass<mlir::func::FuncOp>(mlir::quant::CreateOptimizePass());
}

}  // namespace quantization

  pass_manager->addPass(
      mlir::tf_saved_model::CreateOptimizeGlobalTensorsPass());

  if (pass_config.shape_inference) {
    // Add a shape inference pass to optimize away the unnecessary casts.
    pass_manager->addPass(mlir::TF::CreateTFShapeInferencePass());
  }

  // Legalize while early to allow further constant folding.
  // TODO(jpienaar): This may not actually matter as we do canonicalization

            IsCompatibleTypeWithTFLCastOp(type)) {
          auto cast = b.create<CastOp>(yield_op->getLoc(), type, value);
          args.push_back(cast);
        } else {
          auto cast = b.create<TF::CastOp>(yield_op->getLoc(), type, value);
          args.push_back(cast);
        }
      }
    }
    args.append(new_args.begin(), new_args.end());
  } else {

  return {slice_inputs};
}

// Casts `x` to a DT_INT64 if it isn't one already.
Output MakeInt64(const Scope& host_scope, absl::string_view name,
                 const Output& x) {
  return x.type() == DT_INT64
             ? x
             : ops::Cast(host_scope.WithOpName(name, "_s64"), x, DT_INT64);
}

// Returns `slice_inputs` with the index and size inputs cast to DT_INT64.

        super(ASM_LEVEL, methodVisitor);
    }

    public void emit(BytecodeFragment bytecode) {
        bytecode.emit(mv);
    }

    /**
     * Unboxes or casts the value at the top of the stack.
     */
    public void _UNBOX(Type targetType) {
        switch (targetType.getSort()) {
            case Type.BOOLEAN:

#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/framework/tensor_shape.pb.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/lib/core/coding.h"
#include "tensorflow/core/platform/casts.h"

using tensorflow::Status;
using tensorflow::Tensor;
using tensorflow::TensorBuffer;
using tensorflow::errors::FailedPrecondition;
using tensorflow::errors::InvalidArgument;

#ifndef LIBTPU_EXCLUDE_C_API_IMPL