}

  if (tf_slice_size->dims() != 1) {
    return {std::nullopt};
  }

  slice_inputs.size_as_vector = IntTensorAsVector(*tf_slice_size);
  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

         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.

		{"IfData.Datalen", Field, 0},
		{"IfData.Epoch", Field, 0},
		{"IfData.Hdrlen", Field, 0},
		{"IfData.Hwassist", Field, 0},
		{"IfData.Ibytes", Field, 0},
		{"IfData.Ierrors", Field, 0},
		{"IfData.Imcasts", Field, 0},
		{"IfData.Ipackets", Field, 0},
		{"IfData.Iqdrops", Field, 0},
		{"IfData.Lastchange", Field, 0},
		{"IfData.Link_state", Field, 0},
		{"IfData.Mclpool", Field, 2},

// 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();

#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

	dynamic_go_type = iface_commontype(obj)
	if dynamic_go_type is None:
		return
	return dynamic_go_type['string'].dereference()['str'].string()


class IfacePrinter:
	"""Pretty print interface values

	Casts the data field to the appropriate dynamic type."""

	def __init__(self, val):
		self.val = val

	def display_hint(self):
		return 'string'

	def to_string(self):
		if self.val['data'] == 0:

        checkNotNull(entry.getKey());
        checkNotNull(entry.getValue());
      }
      @SuppressWarnings({"unchecked", "rawtypes"})
      // immutable collections are safe for covariant casts
      // and getting the generics right for EnumMap is difficult to impossible
      ImmutableMap<K, V> result = ImmutableEnumMap.asImmutable(new EnumMap(enumMap));
      return result;
    }