"$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 && "

    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;

    if (!input_type) {
      return failure();
    }
    const ArrayRef<int64_t> input_shape = input_type.getShape();

    const int input_size = input_shape.size();

    RankedTensorType begin_type = mlir::cast<RankedTensorType>(begin.getType());
    const ArrayRef<int64_t> begin_shape = begin_type.getShape();
    const int begin_dim = begin_shape.size();

    if (begin_dim != 1) return failure();

  Type dtype = getElementTypeOrSelf(type);
  if (RankedTensorType ty = type.dyn_cast<RankedTensorType>()) {
    llvm::SmallVector<int64_t, 4> shape = {dim};
    shape.append(ty.getShape().begin(), ty.getShape().end());
    return tensorflow::GetTypeFromTFTensorShape(shape, dtype);
  }
  return type;
}

Type GetTensorTypeForTensorList(Type element_type, TF::VariantType handle_dtype,

    auto input_type = mlir::dyn_cast<ShapedType>(op.getOperand(0).getType());

    llvm::ArrayRef<int64_t> weight_shape = weight_type.getShape();
    llvm::ArrayRef<int64_t> input_shape = input_type.getShape();

    // If weight_shape[2] != 1, it means weight shape was already restored.
    if (weight_shape[2] != 1) return failure();

    // Weight was reshaped into [H, W, 1, InxMul].

  if (!ranked_type)
    return context_op->emitOpError()
           << "A map_outside_compilation op's input and output types must be "
              "ranked tensors.";
  ArrayRef<int64_t> in_shape = ranked_type.getShape();
  if (in_shape.empty() || in_shape[0] < 0) {
    return context_op->emitOpError()
           << "A map_outside_compilation op's input and output shapes must "

    auto output_uniform_quantized_tensor_type = RankedTensorType::getChecked(
        rewriter.getUnknownLoc(),
        /*shape=*/
        mlir::cast<TensorType>(conv_output_value.getType()).getShape(),
        output_uniform_quantized_type);

    SmallVector<Type> new_conv_output_types = {
        output_uniform_quantized_tensor_type};
    auto new_conv_op_with_output_type =

     * <code>null</code> will be returned.
     *
     * @return The share component or <code>null</code> if there is no share
     */
    public String getShare () {
        return this.fileLocator.getShare();
    }


    /**
     * Retrieve the hostname of the server for this SMB resource. If the resources has been resolved by DFS this will
     * return the target name.
     * 

  TF_Buffer* buf = TF_GetAllOpList();
  tensorflow::OpList op_list;
  EXPECT_TRUE(op_list.ParseFromArray(buf->data, buf->length));
  EXPECT_GT(op_list.op_size(), 0);
  TF_DeleteBuffer(buf);
}

TEST(CAPI, SetShape) {
  TF_Status* s = TF_NewStatus();
  TF_Graph* graph = TF_NewGraph();

  TF_Operation* feed = Placeholder(graph, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
  TF_Output feed_out_0 = TF_Output{feed, 0};