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;

          "$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 && "

    auto new_shape_const_attr =
        DenseElementsAttr::get(shape_spec_type, new_shape.getShape());
    rewriter.setInsertionPointAfter(weight_op);
    auto new_shape_const = rewriter.create<TF::ConstOp>(
        weight_op->getLoc(), shape_spec_type, new_shape_const_attr);
    auto reshape_op = rewriter.create<TF::ReshapeOp>(
        weight_op->getLoc(), new_shape, weight_op->getResult(0),
        new_shape_const);

  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,

    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();

      // required by the new converter, from the reshape op's option.
      auto new_shape = op.builtin_options.AsReshapeOptions()->new_shape;
      auto shape_type = tensorflow::GetTypeFromTFTensorShape(
          {static_cast<int64_t>(new_shape.size())}, builder.getIntegerType(32));

      mlir::SmallVector<mlir::Attribute, 4> shape;
      for (auto s : new_shape) {
        shape.push_back(

  }
  llvm::SmallVector<int64_t, 4> shape;
  shape.push_back(split_size);
  for (int i = 1; i < in_shape.size(); ++i) {
    shape.push_back(in_shape[i]);
  }
  shard_type = RankedTensorType::Builder(ranked_type).setShape(shape);
  return mlir::success();
}

// Output `sharding`, which is the sharding of `val`. `context_op` is used for
// error reporting, in case of errors.

func (t *Type) SetDeferwidth(b bool) { t.flags.set(typeDeferwidth, b) }
func (t *Type) SetRecur(b bool)      { t.flags.set(typeRecur, b) }

// Should always do SetHasShape(true) when doing SetIsShape(true).
func (t *Type) SetIsShape(b bool)  { t.flags.set(typeIsShape, b) }
func (t *Type) SetHasShape(b bool) { t.flags.set(typeHasShape, b) }

// Kind returns the kind of type t.
func (t *Type) Kind() Kind { return t.kind }

        print('TODO: use "node_type"')
      if node.attr == 'shape' and tensor_type == TFRTypes.TENSOR:
        ssa_value = self._ssa_name('shape')
        self._emit_with_loc(
            '\n{} = tfr.get_shape {} -> !shape.shape'.format(ssa_value, value),
            node)
        return (ssa_value, TFRTypes.SHAPE)

    if isinstance(node.value, ast.Attribute):
      if isinstance(node.value.value, ast.Name):

    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 =