flow_type.getRank() != 4) {
    return func_.emitWarning() << "Flow should be a 4D float tensor";
  }

  auto output_type = mlir::dyn_cast_or_null<RankedTensorType>(
      func_.getFunctionType().getResult(0));
  if (!output_type || !output_type.getElementType().isF32() ||
      output_type.getRank() != 4) {
    return func_.emitWarning() << "Output should be a 4D float tensor";
  }

  return success();
}

               << ", output_inference_type: "
               << tflite::EnumNameTensorType(output_type) << "\n";
  mlir::Builder mlir_builder(&context);
  mlir::Type input_mlir_type =
      tflite::ConvertElementType(input_type, mlir_builder);
  mlir::Type output_mlir_type =
      tflite::ConvertElementType(output_type, mlir_builder);

  if (fully_quantize) {

    if (!input_dequant) return failure();

    if (!IsQI32Type(input_dequant.getType())) return failure();

    auto output_type =
        mlir::dyn_cast_or_null<ShapedType>(dequant_op.getOutput().getType());
    if (!output_type || !output_type.getElementType().isF32()) return failure();

    auto input_type = mlir::dyn_cast<ShapedType>(input_dequant.getType());

    ArrayRef<int64_t> reshape_shape = reshape_type.getShape();

    auto input_type = mlir::cast<ShapedType>(op.getInput().getType());
    auto output_type = mlir::cast<ShapedType>(op.getOutput().getType());

    if (!input_type.hasRank() || !output_type.hasRank()) return failure();

    // The pattern attempts to reduce the rank of the input to BroadcastTo.
    // Thus, we fail to match if the consuming reshape rank is larger.

  }

  for (auto result : llvm::zip(getResultTypes(), getOutputSpecs())) {
    Type outputType = std::get<0>(result);
    Attribute outputSpec = std::get<1>(result);
    if (!isValidQuantizationSpec(outputSpec, outputType)) {
      return emitOpError() << "has incompatible specification " << outputSpec
                           << " and output type " << outputType;
    }
  }
  return success();
}

LogicalResult StatisticsOp::verify() {

  }

  for (auto result : llvm::zip(getResultTypes(), getOutputSpecs())) {
    Type outputType = std::get<0>(result);
    Attribute outputSpec = std::get<1>(result);
    if (!isValidQuantizationSpec(outputSpec, outputType)) {
      return emitOpError() << "has incompatible specification " << outputSpec
                           << " and output type " << outputType;
    }
  }
  return success();
}

LogicalResult StatisticsOp::verify() {

  TF_RETURN_IF_ERROR(OutputTypesForNode(node_def, *op_def, &output_dtys));

  mlir::MLIRContext* context = tfr_module_.getContext();
  llvm::SmallVector<mlir::Type, 4> input_tys, output_tys;
  mlir::Builder builder(context);
  for (auto ty : input_dtys) {
    mlir::Type elt_ty;
    TF_RETURN_IF_ERROR(ConvertDataType(ty, builder, &elt_ty));

 public:
  using OpRewritePattern<TF::HashTableV2Op>::OpRewritePattern;

  LogicalResult matchAndRewrite(TF::HashTableV2Op hashtable_op,
                                PatternRewriter& rewriter) const override {
    auto output_type = RankedTensorType::get(
        {1}, TF::ResourceType::get(rewriter.getContext()));

    // Hash the shared name to generate integer hash table id. The TFLite

        !bmm_op.getAdjY() ? create_z_x_transpose_op(input_rhs) : input_rhs;

    Type output_type = bmm_op.getResult().getType();
    auto no_input = rewriter.create<TFL::NoValueOp>(
        bmm_op->getLoc(), rewriter.getNoneType(), rewriter.getUnitAttr());
    auto fc_op = rewriter.create<TFL::FullyConnectedOp>(
        bmm_op->getLoc(), ArrayRef<Type>{output_type},
        /*input=*/output_lhs, /*filter=*/output_rhs, /*bias=*/no_input,

        failure.assertHasCause("Could not create task ':myTask'.")
        failure.assertHasCause("Could not create task of type 'CustomTask'.")

        where:
        description | constructorArgs | argumentNumber | outputType
        'first'     | '123, 234'      | 1              | 'class java.lang.String'
        'last'      | '"abc", "123"'  | 2              | 'int'
    }