}
  }

  const DimensionVector& batch_dimensions() const { return batch_dimensions_; }
  const DimensionVector& contracting_dimensions() const {
    return contracting_dimensions_;
  }
  // Out dimensions are any dimensions that are neither batch nor contracting
  // dimensions, hence will be propagated to output shape.
  const DimensionVector& out_dimensions() const { return out_dimensions_; }

// Returns a TF Constant tensor with the passed in values.
TF::ConstOp GetI64ConstantTensor(PatternRewriter &rewriter,
                                 ArrayRef<int64_t> values, Location location) {
  auto cst_attr = rewriter.getI64TensorAttr(values);
  return rewriter.create<TF::ConstOp>(location, cst_attr.getType(), cst_attr);
}

// Rewrites broadcast->reshape to a reshape->broadcast that reduces

  auto assigned_value_operand = assign_var_op.getOperand(1);
  auto const_op =
      llvm::dyn_cast<mlir::TF::ConstOp>(assigned_value_operand.getDefiningOp());
  if (!const_op) {
    assign_var_op->emitRemark(
        "Operand idx 1 is not a tf.ConstOp. The initializing tensor is not "
        "saved to checkpoint.");
    return "";
  }

  Tensor const_tensor{};
  if (const absl::Status status = mlir::tfg::ConvertToTensor(

      auto conv_result = rewriter.create<mhlo::ConvolutionOp>(
          conv_op.getLoc(), new_output_type, sliced_input, sliced_kernel,
          conv_op.getWindowStridesAttr(), conv_op.getPaddingAttr(),
          conv_op.getLhsDilationAttr(), conv_op.getRhsDilationAttr(),
          conv_op.getWindowReversalAttr(), conv_op.getDimensionNumbers(), 1, 1,
          conv_op.getPrecisionConfigAttr());

      conv_results.push_back(conv_result);

                 .getOperation();
           });

  // mlir::TF::ConstOp
  py::class_<mlir::TF::ConstOp>(m, "Tf_ConstOp")
      .def("create",
           [](mlir::OpBuilder& opb, mlir::Location loc,
              mlir::Attribute value) -> mlir::Operation* {
             return opb.create<mlir::TF::ConstOp>(loc, value).getOperation();
           });

  // mlir::TF::EqualOp

  if (!type) return -1;
  auto indices_op = indices.getDefiningOp();
  if (!indices_op) return -1;
  auto const_op = llvm::dyn_cast<TF::ConstOp>(indices_op);
  if (!const_op) return -1;
  int64_t last_index = -1;
  int64_t first_index = -1;
  for (const auto& ind : const_op.getValue().getValues<APInt>()) {
    if (last_index == -1) {
      last_index = ind.getSExtValue();
      first_index = last_index;

    if (isa<TF::VarHandleOp>(resource_operand.getDefiningOp()) &&
        isa<TF::ConstOp>(assigned_value_operand.getDefiningOp())) {
      return success();
    } else {
      return failure();
    }
  }

  void rewrite(TF::AssignVariableOp assign_op,
               PatternRewriter& rewriter) const override {
    // `TF::ConstOp` and `TF::VarHandleOp` are not manually erased.

    TFL::Conv2DOp conv_op, PatternRewriter& rewriter) const {
  return EnsureBias(conv_op, 2, rewriter);
}

// ================== slice ============================

// If a slice op has < 4d dimension, will pad it to 4d.
LogicalResult PadSlice::matchAndRewrite(TFL::SliceOp slice_op,
                                        PatternRewriter& rewriter) const {

        RankedTensorType type = RankedTensorType::get(
            {}, TF::StringType::get(builder.getContext()));
        auto const_op = builder.create<TF::ConstOp>(
            builder.getUnknownLoc(),
            DenseStringElementsAttr::get(type, {filename}));

        it = const_ops.insert({asset.getSymName(), const_op}).first;
      }

      argument.replaceAllUsesWith(it->second.getOutput());

          << " does not have a defining op and cannot be duplicated.";
    } else {
      // If the operand's defining is not a ConstOp, recursively traverse
      // "upwards" to find ConstOps that transitively produces the current
      // operand and duplicate them.
      auto op_operands = defining_op->getOpOperands();
      absl::c_transform(