"$0.getType().cast<ShapedType>().getShape() =="
          "$1.getType().cast<ShapedType>().getShape()">,
    "have the same static shape">;

def IsPermutationNCHW : Constraint<CPred<"IsPermutationNCHW($0)">>;

def IsBiasShape : Constraint<
    CPred<"$0.getType().cast<ShapedType>().getRank() == 4 && "
          "$0.getType().cast<ShapedType>().getShape()[2] == 1 && "

    // Thus, we fail to match if the consuming reshape rank is larger.
    ArrayRef<int64_t> input_shape = input_type.getShape();
    if (reshape_shape.size() > input_shape.size()) return failure();

    // Extend the input shape with leading 1s to match the broadcast shape.
    ArrayRef<int64_t> broadcast_shape = output_type.getShape();
    SmallVector<int64_t, 4> input_shape_extended;

  Value temp_padding =
      CreateConstValue<int32_t>(builder, loc, {num_dims, 2}, padding_values);
  SmallVector<int64_t> output_shape(input_shape.getShape().begin(),
                                    input_shape.getShape().end());
  for (int i : spatial_dims) {
    output_shape[i] += padding_values[2 * i] + padding_values[2 * i + 1];
  }

  return builder.create<TF::PadV2Op>(

  auto output_types = fused_lstm_func_.getFunctionType().getResults();
  SmallVector<int64_t, 2> output_shape{1, mlir::ShapedType::kDynamic};
  EXPECT_EQ(mlir::cast<RankedTensorType>(output_types[0]).getShape().size(),
            output_shape.size());
  for (int i = 0; i < output_shape.size(); i++) {
    EXPECT_EQ(mlir::cast<RankedTensorType>(output_types[0]).getDimSize(i),
              output_shape[i]);
  }
}

          bmm_op->getLoc(), permuation_tensor_type,
          DenseElementsAttr::get(permuation_tensor_type, permute));

      auto input_shape = input_type.getShape();
      llvm::SmallVector<int64_t, 4> permuted_shape(input_shape.begin(),
                                                   input_shape.end());
      // Swaps z dimension and x dimension to get permuted shape.

// ShapeContainerT is any type with the following methods:
//   bool hasRank()
//   ArrayRef<int64_t> getShape()
// This includes mlir::TF::ShapeAttr and mlir::ShapedType.
template <typename ShapeContainerT>
void SetTensorShapeProto(ShapeContainerT shape, TensorShapeProto* proto) {
  if (shape.hasRank()) {
    for (int64_t dim : shape.getShape()) {
      proto->add_dim()->set_size(mlir::ShapedType::isDynamic(dim) ? -1 : dim);
    }
  } else {

  rhs_shape.reserve(dnums.lhs_rhs_out.size() + 2);
  for (auto i : dnums.lhs_rhs_out) {
    const int64_t b1 = lhs_type.getShape()[std::get<0>(i)];
    lhs_shape.push_back(b1);
    const int64_t b2 = rhs_type.getShape()[std::get<1>(i)];
    rhs_shape.push_back(b2);

    lhs_reshape_segids.at(std::get<0>(i)) = num_lhs_reshape_segids++;

LogicalResult HandlePad(TF::PadOp op, int32_t kernel_size, int32_t block_size) {
  auto ranked_type = mlir::dyn_cast<RankedTensorType>(op.getInput().getType());
  if (!ranked_type) return failure();
  auto pad_input_shape = ranked_type.getShape();
  Location loc = op.getLoc();
  OpBuilder builder(op);
  builder.setInsertionPoint(op);
  auto padding_type = RankedTensorType::get({4, 2}, builder.getIntegerType(32));

  // Calculate paddings.

    return operand_ty == result_ty && identity_ty.hasStaticShape() &&
           result_ty.hasStaticShape() &&
           OpTrait::util::staticallyKnownBroadcastable(operand_ty.getShape(),
                                                       identity_ty.getShape());
  };

  // Check that we have a constant operand on one side (candidate for identity).
  const bool is_commutative =

    if (!input_ty.hasRank() || input_ty.getRank() != 4) {
      return failure();
    }

    int64_t batch_cst = input_ty.getShape()[0];
    int64_t channels_cst = input_ty.getShape()[3];

    int64_t in_y_cst = input_ty.getShape()[1];
    int64_t in_x_cst = input_ty.getShape()[2];
    int64_t in_spatial_cst =
        in_y_cst < 0 || in_x_cst < 0 ? -1 : in_y_cst * in_x_cst;