GradientFunction* gradient_function,
                           const string& op_name) {
  std::vector<int64_t> input_ids(inputs.size());
  std::vector<tensorflow::DataType> input_dtypes(inputs.size());
  for (int i = 0; i < inputs.size(); i++) {
    input_ids[i] = ToId(inputs[i]);
    input_dtypes[i] = inputs[i]->DataType();
  }
  std::vector<TapeTensor> tape_tensors;

const char* TF_GetNumberAttrForOpListInput(const char* op_name, int input_index,
                                           TF_Status* status) {
  const tensorflow::OpDef* op_def = nullptr;
  status->status =
      tensorflow::OpRegistry::Global()->LookUpOpDef(op_name, &op_def);
  if (!status->status.ok()) return nullptr;

  if (input_index >= op_def->input_arg_size() || input_index < 0) {
    status->status = tensorflow::errors::InvalidArgument(

    return false;
  }
  if (filter_index == input_indices[0]) {
    input_index = input_indices[1];
  } else if (filter_index == input_indices[1]) {
    input_index = input_indices[0];
  } else {
    return false;
  }

  const QuantState& input_state = GetOperandQuantState(op, input_index);
  const QuantState& filter_state = GetOperandQuantState(op, filter_index);

      ops::Placeholder(root.WithOpName("input_float"), DT_FLOAT);
  Output input_i64 = ops::Placeholder(root.WithOpName("input_i64"), DT_INT64);

  Output begin_begin =
      ops::Placeholder(root.WithOpName("begin_begin"), DT_INT32);
  Output begin_size = ops::Const(root.WithOpName("begin_size"), {-1});
  Output begin =
      ops::Slice(root.WithOpName("begin"), input_i64, begin_begin, begin_size);

  Output size =

// point is dynaically calculated so not a constant.
def ConvertTFConv2DToXLAConvOpDynamicRange : Pat<
  (TF_Conv2DOp:$conv
    (TF_SubOp:$input (TF_CastOp $input_i8, $truncate0), $input_zp),
    (TF_CastOp (TF_IdentityOp $filter), $truncate1),
    $strides, $use_cudnn, $padding, $explicit_padding,
    IsDataFormatNHWC:$data_format, $dilations),
  (CreateXLAConvOpFromTFConv2DOp

    if (!outputs[i]) continue;

    AbstractTensorHandlePtr numerical_grad;
    {
      AbstractTensorHandle* numerical_grad_raw;
      s = CalcNumericalGrad(ctx, model, inputs,
                            /*input_index=*/i, use_function,
                            &numerical_grad_raw);
      ASSERT_EQ(errors::OK, s.code()) << s.message();
      numerical_grad.reset(numerical_grad_raw);
    }

    TF_Tensor* numerical_tensor;

  // the input.
  for (const auto& sharding_attr_and_index : llvm::enumerate(sharding_attrs)) {
    const auto& sharding_attr = sharding_attr_and_index.value();
    const auto input_index = sharding_attr_and_index.index();
    const auto& input_value = cluster_func_inputs[input_index];

    xla::OpSharding sharding;
    if (DecodeShardingAttribute(
            mlir::cast<mlir::StringAttr>(sharding_attr).getValue().str(),
            sharding)

    func.return %add : tensor<*xf32>
  }

  func.func private @internal_dequantize_i8_fn(%input : tensor<*xi8>, %scale : tensor<*xf32>, %zp : tensor<*xi32>) -> tensor<*xf32> {
    %input_i32 = "tf.Cast"(%input) : (tensor<*xi8>) -> tensor<*xi32>
    %output = "tf.Sub"(%input_i32, %zp) : (tensor<*xi32>, tensor<*xi32>) -> tensor<*xi32>
    %cast = "tf.Cast"(%output) : (tensor<*xi32>) -> tensor<*xf32>

                                  previous_unknown_resource_access)) {
        VLOG(2) << "  removing op from merge candidates";
        int input_index = info_it->getSecond().execute_input_index;
        var_access_info.new_operand_values[input_index] =
            execute.getOperand(input_index);
        var_access_info.per_resource_info.erase(info_it);
      }
    }
    previous_unknown_resource_access |=

// For argument number input_index, fetch the corresponding number_attr that
// needs to be updated with the argument length of the input list.
// Returns nullptr if there is any problem like op_name is not found, or the
// argument does not support this attribute type.
TF_CAPI_EXPORT extern const char* TF_GetNumberAttrForOpListInput(
    const char* op_name, int input_index, TF_Status* status);