String thisType = artifact.getType();

                        if (currentDefault == null) {
                            defaultArtifacts.add(artifact);
                            currentDefault = artifact;
                        } else {
                            String currentType = currentDefault.getType();
                            if (thisType.equals("ear")) {

        return instrumentationTypes.contains(bytecodeInterceptor.getType());
    }

    public boolean matches(FilterableBytecodeInterceptorFactory bytecodeInterceptorFactory) {
        return instrumentationTypes.contains(bytecodeInterceptorFactory.getType());
    }

    Value input_lhs = op.getX();
    Value input_rhs = op.getY();

    // LHS and RHS must be a ranked tensor type
    auto lhs_type = mlir::dyn_cast<RankedTensorType>(input_lhs.getType());
    auto rhs_type = mlir::dyn_cast<RankedTensorType>(input_rhs.getType());

    if (!lhs_type || !rhs_type) return failure();

    auto lhs_shape = lhs_type.getShape();
    auto rhs_shape = rhs_type.getShape();

            boolean isArray = param.getType().endsWith("[]");
            boolean isCollection = false;
            boolean isMap = false;
            boolean isProperties = false;
            if (!isArray) {
                try {
                    // assuming Type is available in current ClassLoader
                    isCollection = Collection.class.isAssignableFrom(Class.forName(param.getType()));

    outConvertedType = converted.getType();
    return converted;
  }
  if (mlir::isa<SparseElementsAttr>(realValue)) {
    // Sparse tensor or vector constant.
    auto converted =
        convertSparseElementsAttr(mlir::cast<SparseElementsAttr>(realValue),
                                  quantizedElementType, converter);
    outConvertedType = converted.getType();
    return converted;
  }

      // the requantize op to remove the unnecessary requantize op.
      if (auto qtype =
              QuantizedType::getQuantizedElementType(q.getArg().getType())) {
        rewriter.setInsertionPoint(op);
        rewriter.replaceOpWithNewOp<quantfork::DequantizeCastOp>(
            op, op.getResult().getType(), q.getArg());
        return success();
      }

      op.replaceAllUsesWith(q.getArg());
      return success();
    }

      if (IsLargeFloatType(op_operand.get().getType())) {
        op_operand.set(builder.create<TF::CastOp>(
            op->getLoc(), ToBfloat16Type(op_operand.get().getType()),
            op_operand.get()));
      }
    }
    builder.setInsertionPointAfter(op);
    for (auto op_result : op->getOpResults()) {
      if (IsLargeFloatType(op_result.getType())) {
        const Type original_type = op_result.getType();

        if (expression.getType() == IncludeType.MACRO_FUNCTION && !expression.getArguments().isEmpty()) {
            return new IncludeWithMacroFunctionCallExpression(expression.getValue(), isImport, ImmutableList.copyOf(expression.getArguments()));
        }
        return create(expression.getValue(), isImport, expression.getType());
    }

                        modelNode.addProjection(new SpecializedModelMapProjection<T, E>(schema.getType(), schema.getElementType(), implementationType, strategyAccessor));
                        modelNode.addProjection(ModelMapModelProjection.managed(schema.getType(), schema.getElementType(), strategyAccessor));
                    }
                }

}

// This corresponds to tensorflow::Tensor.
def TFTensorType : DialectType<Fallback_Dialect,
    CPred<"$_self.isa<::tfrt::fallback::TFTensorType>()">, "!tfrt_fallback.tf_tensor type">,
    BuildableType<"$_builder.getType<::tfrt::fallback::TFTensorType>()"> {
  let description = [{
    `!tfrt_fallback.tf_tensor type` represents a tensorflow::Tensor.
  }];
}

// This corresponds to tensorflow::Allocator.