// converting back from a quantized to quantizable (expressed) type.
//
// Like qcasts, a dcast is allowed to have both its operand and result
// as non quantized types. This facilitates transformations and marks edges
// where the computation must be carried out in the expressed type.
//
// Especially early in transformation, it is common to have dcasts on
// all operands to ops that must operate with the expressed type (typically

  %0 = "tf.IfRegion"(%arg0) ({
    %1 = "tf.Cast"(%arg1) {Truncate = false} : (tensor<2xf32>) -> tensor<?xf32>
    %2 = "tf.Cast"(%1) {Truncate = false} : (tensor<?xf32>) -> tensor<*xf32>
    %3 = func.call @testIf1Then(%2) : (tensor<*xf32>) -> tensor<*xf32>
    "tf.Yield"(%3) : (tensor<*xf32>) -> ()
  },  {
    %1 = "tf.Cast"(%arg1) {Truncate = false} : (tensor<2xf32>) -> tensor<?xf32>

#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/lib/gtl/flatmap.h"
#include "tensorflow/core/lib/hash/hash.h"
#include "tensorflow/core/platform/casts.h"
#include "tensorflow/core/platform/errors.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/macros.h"
#include "tensorflow/core/platform/path.h"

// output model of this pass is expected to be ready for the TF quantizer.
std::unique_ptr<OperationPass<ModuleOp>> CreateConvertTpuModelToCpuPass();

// Creates a pass that casts BFloat16 operations to Float32 operations. This
// pass is a part of the ConvertTpuModelToCpu pass to support BF16 optimized TPU
// model quantization.
std::unique_ptr<OperationPass<ModuleOp>> CreateCastBf16OpsToF32Pass();

         llvm::all_of(op->getOperands(), IsQintValueDefinedByIntToQintCast);
}

bool IsCastOpLegal(TF::CastOp cast_op) {
  // Consider qint <-> qint casts illegal.
  if (IsIllegalType(cast_op.getSrcT()) && IsIllegalType(cast_op.getDstT())) {
    return false;
  }
  // Consider CastOp illegal if either of its Src/Dst type is qint and is
  // connected to a non-UQ op.

#include "tensorflow/compiler/mlir/lite/utils/const_tensor_utils.h"

#include <cassert>
#include <cstddef>
#include <cstdint>
#include <limits>
#include <string>
#include <vector>

#include "absl/base/casts.h"
#include "absl/status/status.h"
#include "absl/strings/str_cat.h"
#include "Eigen/Core"  // from @eigen_archive
#include "llvm/ADT/APInt.h"
#include "llvm/ADT/SmallVector.h"
#include "llvm/ADT/bit.h"

#include "tensorflow/c/eager/tfe_tensorhandle_internal.h"
#include "tensorflow/core/common_runtime/eager/eager_operation.h"
#include "tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h"
#include "tensorflow/core/platform/casts.h"
#include "tensorflow/core/platform/protobuf.h"
#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/protobuf/cluster.pb.h"
#include "tensorflow/core/protobuf/tensorflow_server.pb.h"

namespace {

    }

    @Override
    public K[] createArray(int length) {
      // TODO: with appropriate refactoring of OneSizeGenerator, we can perhaps
      // tidy this up and get rid of the casts here and in
      // MapValueCollectionGenerator.

      return ((TestMapGenerator<K, V>) mapGenerator.getInnerGenerator()).createKeyArray(length);
    }

    @Override

    }

    @Override
    public K[] createArray(int length) {
      // TODO: with appropriate refactoring of OneSizeGenerator, we can perhaps
      // tidy this up and get rid of the casts here and in
      // MapValueCollectionGenerator.

      return ((TestMapGenerator<K, V>) mapGenerator.getInnerGenerator()).createKeyArray(length);
    }

    @Override

#include "tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h"
#include "tensorflow/core/framework/function.h"
#include "tensorflow/core/graph/graph.h"
#include "tensorflow/core/platform/casts.h"
#include "tensorflow/core/platform/errors.h"
#include "tensorflow/core/platform/protobuf.h"
#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/protobuf/cluster.pb.h"