using ::mlir::quant::IsDotGeneralFullyConnected;
using ::mlir::quant::IsI32F32UniformQuantizedPerAxisType;
using ::mlir::quant::IsI32F32UniformQuantizedType;
using ::mlir::quant::IsI8F32UniformQuantizedPerAxisType;
using ::mlir::quant::IsI8F32UniformQuantizedType;
using ::mlir::quant::IsOpFullyQuantized;
using ::mlir::quant::IsQuantizedTensorType;
using ::mlir::quant::IsSupportedByTfliteQuantizeOrDequantizeOps;
using ::mlir::quant::QuantizedType;

using tensorflow::PartialTensorShape;
using tensorflow::RunMetadata;
using tensorflow::RunOptions;
using tensorflow::Session;
using tensorflow::Status;
using tensorflow::string;
using tensorflow::Tensor;
using tensorflow::TensorId;
using tensorflow::TensorShapeProto;
using tensorflow::VersionDef;
using tensorflow::errors::FailedPrecondition;
using tensorflow::errors::InvalidArgument;
using tensorflow::errors::OutOfRange;

      }
    }

    return true;
  }
};

using ScalarizeSplatConstantForSub =
    ScalarizeSplatConstantForBroadcastableOps<TFL::SubOp>;
using ScalarizeSplatConstantForAdd =
    ScalarizeSplatConstantForBroadcastableOps<TFL::AddOp>;
using ScalarizeSplatConstantForMul =
    ScalarizeSplatConstantForBroadcastableOps<TFL::MulOp>;
using ScalarizeSplatConstantForDiv =

    rewriter.replaceOpWithNewOp<mhlo::ReshapeOp>(
        conv_op, output_type, transposed_output_2d_op.getResult());
    return success();
  }
};

using Convert2DConvOp = ConvertNdConvOp<2>;
using Convert3DConvOp = ConvertNdConvOp<3>;

// Utility function to check for supported non-trivial convolutions with
// lhs_dilation>1 and window_strides=1.

#include "tsl/platform/tstring.h"

using absl::StatusOr;
using llvm::dyn_cast;
using llvm::formatv;
using llvm::isa;
using llvm::StringRef;
using mlir::Dialect;
using mlir::ElementsAttr;
using mlir::MLIRContext;
using mlir::ModuleOp;
using mlir::NoneType;
using mlir::Operation;
using mlir::Region;
using mlir::StringAttr;
using mlir::TensorType;
using mlir::Twine;
using mlir::Type;

// those terms (in addition to any explicit constraints in the file).
//
// Using GOOS=android matches build tags and files as for GOOS=linux
// in addition to android tags and files.
//
// Using GOOS=illumos matches build tags and files as for GOOS=solaris
// in addition to illumos tags and files.
//
// Using GOOS=ios matches build tags and files as for GOOS=darwin
// in addition to ios tags and files.
//

			if !reflect.DeepEqual(got, expected) {
				t.Errorf("using %s error in test case: %s\ncannot sort object:\n%s\nexpected:\n%s\ngot:\n%s\n",
					getSchemaType(schema), c.Description, mergepatch.ToYAMLOrError(c.Original), mergepatch.ToYAMLOrError(c.Sorted), jsonToYAMLOrError(got))
			}
		}
	}
}

// These are test cases for StrategicMergePatch that cannot be generated using

field is not supported if watch is true.\n\nThe server guarantees that the objects returned when using continue will be identical to issuing a single list call without a limit - that is, no objects created, modified, or deleted after the first request is issued will be included in any subsequent continued requests. This is sometimes referred to as a consistent snapshot, and ensures that a client that is using limit to receive smaller chunks of a very large result can ensure they see all possible objects....

field is not supported if watch is true.\n\nThe server guarantees that the objects returned when using continue will be identical to issuing a single list call without a limit - that is, no objects created, modified, or deleted after the first request is issued will be included in any subsequent continued requests. This is sometimes referred to as a consistent snapshot, and ensures that a client that is using limit to receive smaller chunks of a very large result can ensure they see all possible objects....

field is not supported if watch is true.\n\nThe server guarantees that the objects returned when using continue will be identical to issuing a single list call without a limit - that is, no objects created, modified, or deleted after the first request is issued will be included in any subsequent continued requests. This is sometimes referred to as a consistent snapshot, and ensures that a client that is using limit to receive smaller chunks of a very large result can ensure they see all possible objects....