%weight_scales = "tf.Const"() { value = dense<1.0> : tensor<f32> } : () -> tensor<f32>
  %weight_zps = "tf.Const"() { value = dense<3> : tensor<i32> } : () -> tensor<i32>

  %output_scales = "tf.Const"() { value = dense<3.0> : tensor<f32> } : () -> tensor<f32>
  %output_zps = "tf.Const"() { value = dense<5> : tensor<i32> } : () -> tensor<i32>

  // CHECK-DAG: %[[RHS:.*]] = mhlo.constant()

   def FuseBinaryOpWithTransposeConv#binaryOp : Pat<
    (binaryOp (TFL_TransposeConvOp:$output $output_shape, $weights, $input,
                (Arith_ConstantOp FloatElementsAttr:$bias), $padding,
                $stride_h, $stride_w, TFL_AF_None),
              (Arith_ConstantOp FloatElementsAttr:$value), $act_fn),
    (TFL_TransposeConvOp $output_shape, $weights, $input,
      (binaryOp (Arith_ConstantOp $bias),

    TensorOf<[TF_Qint8]>:$rhs,
    TF_Float32Tensor:$lhs_scales,
    TF_Int32Tensor:$lhs_zps,
    TF_Float32Tensor:$rhs_scales,
    TF_Int32Tensor:$rhs_zps,
    TF_Float32Tensor:$output_scales,
    TF_Int32Tensor:$output_zps,

    StrAttr:$dimension_numbers,
    DefaultValuedOptionalAttr<I64Attr, "-1">:$lhs_quantization_axis,
    DefaultValuedOptionalAttr<I64Attr, "-128">:$lhs_quantization_min_val,

    auto new_shape = rewriter.create<TF::ShapeOp>(loc, shape_type, input);
    SmallVector<int64_t, 8> output_shape(/*Size=*/1, op.getNumElements());
    for (const auto &dim : dense_elem_attr.getValues<APInt>())
      output_shape.push_back(dim.getSExtValue());
    RankedTensorType result_type = tensorflow::GetTypeFromTFTensorShape(
        output_shape, getElementTypeOrSelf(input));
    rewriter.replaceOpWithNewOp<TF::ReshapeOp>(op, result_type, input,

  bool restrict_functionalization_to_compiled_nodes = false;
  // If true, enables shape inference on input.
  // TODO(jpienaar): This will be removed shortly.
  bool enable_shape_inference = true;
  // _output_shapes is an unregistered attribute which is used during
  // GraphConstructor::ConvertGraph to override shapes. It is unfortunately
  // not always set correctly (which is undesirable and should be addressed)

      auto begin =
          rewriter.create<ConstOp>(op->getLoc(), axis_type, begin_attr);

      SmallVector<int64_t, 4> output_shape;
      output_shape.append(input_shape.begin(), input_shape.end());
      output_shape[axis_i] = size_i;
      auto size_attr = DenseIntElementsAttr::get(axis_type, output_shape);
      auto size = rewriter.create<ConstOp>(op->getLoc(), axis_type, size_attr);

  // Create a bad output shape attr.
  AttrValue shape_attr;
  TensorShapeProto* shape_proto = shape_attr.mutable_list()->add_shape();
  shape_proto->add_dim()->set_size(1);
  builder.Attr("_output_shapes", shape_attr);

  TF_RETURN_IF_ERROR(builder.Finalize(&node));

  return CreateSingleOpGraph(node, {}, {DataType::DT_INT32});
}

absl::Status BuildHloFromGraph(Graph& graph, bool use_output_shapes) {

  int64_t out_row_dim = output_shape[output_shape.size() - 2];
  int64_t out_col_dim = output_shape[output_shape.size() - 1];

  int64_t expected_out_row_dim = op.getAdjX() ? x_col_dim : x_row_dim;
  int64_t expected_out_col_dim = op.getAdjY() ? y_row_dim : y_col_dim;

  if (expected_out_row_dim != ShapedType::kDynamic &&
      out_row_dim != ShapedType::kDynamic &&
      out_row_dim != expected_out_row_dim)

  // n_input_entries/n_output_entries
  // = group_size
  auto input_shape = Shape(scope, op.input(0));
  auto output_shape = Shape(scope, op.output(0));
  auto zero = Const(scope, 0);
  auto group_size = SafeDivHelper(scope, Prod(scope, input_shape, zero),
                                  Prod(scope, output_shape, zero));

  // propagate sum_grad/group_size
  grad_outputs->push_back(

  return ConvertTensor(t, builder);
}

void ConvertToTensorShapeProto(ArrayRef<int64_t> shape,
                               TensorShapeProto* output_shape) {
  for (auto d : shape) {
    output_shape->add_dim()->set_size(ShapedType::isDynamic(d) ? kTFDynamicSize
                                                               : d);
  }
}