for (auto x : llvm::enumerate(broadcast_dimensions)) {
    expanded_shape[x.value().getSExtValue()] =
        rewriter.getI64IntegerAttr(input_shape[x.index()]);
  }

  // Create the expanded type wrapped in a arith::ConstantOp.
  auto attr_type =
      RankedTensorType::get({static_cast<int64_t>(expanded_shape.size())},
                            rewriter.getIntegerType(64));

        mlir::cast<ShapedType>(stb_op.getInput().getType()).getShape();
    SmallVector<int64_t, 4> expand_shape(input_shape.begin(),
                                         input_shape.end());
    expand_shape.insert(expand_shape.begin() + expand_axis, 1);

    auto expand_result_type = RankedTensorType::get(
        expand_shape, getElementTypeOrSelf(stb_op.getInput()));
    expand_op.getResult().setType(expand_result_type);

    "new dimensions are inserted in intermediate positions">;

// Return intermediate shape before broadcasting, wrapped in a constant op.
def ExpandedShape : NativeCodeCall<"ExpandedShape($_builder, $0, $1, $2)">;
def ExpandedDynamicShape : NativeCodeCall<"ExpandedDynamicShape($_builder, $0, $1, $2)">;

def : Pat<(MHLO_ConstantOp:$output $value), (TF_ConstOp $value),

                                    filter_shape[1] + pad_w, filter_shape[2],
                                    filter_shape[3]};
  SmallVector<int64_t, 4> expand_shape(pad_shape.begin(), pad_shape.end());

  auto expand_result_type =
      RankedTensorType::get(expand_shape, getElementTypeOrSelf(filter));
  return builder->create<TF::PadOp>(filter.getLoc(), expand_result_type, filter,
                                    paddings_value);

// CHECK-NEXT:  [[ZERO_2:%.*]] = arith.constant dense<0> : tensor<i32>
// CHECK-NEXT:  [[EXTENDED_SHAPE:%.*]] = "tf.Concat"([[ZERO_2]], [[EXPANDED_SIZE_DIFF]], [[ELEM_SHAPE]]) : (tensor<i32>, tensor<1xi32>, tensor<?xi32>) -> tensor<2xi32>
// CHECK-NEXT:  [[ZERO_FLOAT:%.*]] = arith.constant dense<0.000000e+00> : tensor<f32>
// CHECK-NEXT:  [[EXTENDED_PART:%.*]] = "tf.Fill"([[EXTENDED_SHAPE]], [[ZERO_FLOAT]]) : (tensor<2xi32>, tensor<f32>) -> tensor<?x10xf32>