ImplicitLocOpBuilder& builder,
                                      bool is_lhs) {
  auto operand_type = mlir::cast<ShapedType>(operand.getType());
  BoolAttr true_attr = builder.getBoolAttr(true);
  auto operand_shape = builder.create<TF::ShapeOp>(operand, true_attr);
  const int64_t operand_rank = operand_type.getRank();

           << "]";

  return success();
}

bool VariableIsInitialized(TF::VarHandleOp var_handle_op) {
  auto is_variable_initialized =
      var_handle_op->getAttrOfType<BoolAttr>("_is_initialized");
  // Assume variable is initialized if attribute is not set.
  // There are paths that doesn't mark the variables. All variables
  // that doesn't have the attribute will be promoted.

  NativeCodeCall<"GetI64ElementsAttr({" # value # "}, &$_builder)">;

class GetF32Scalar<int value> :
  NativeCodeCall<"GetF32Scalar(&$_builder, " # value # ")">;

def TrueBoolAttr : AttrConstraint<CPred<"$_self.cast<::mlir::BoolAttr>().getValue()">>;

def CreateTFShapeOp : NativeCodeCall<
    "$_builder.create<TF::ShapeOp>($0.getLoc(), $1, $2)">;

def IsI32 : NativeCodeCall<

    return true;
  }

  OpBuilder builder(func);
  bool is_signed = quant_specs_.IsSignedInferenceType();
  IntegerAttr num_bits =
      builder.getI32IntegerAttr(quant_specs_.GetQuantizationTypeWidth());
  BoolAttr narrow_range = builder.getBoolAttr(false);

  auto add_quantize_op = [&](Location loc, Type input_type, Block* block,
                             Block::iterator insertion_point, Value arg,

    return true;
  }

  OpBuilder builder(func);
  bool is_signed = quant_specs_.IsSignedInferenceType();
  IntegerAttr num_bits =
      builder.getI32IntegerAttr(quant_specs_.GetQuantizationTypeWidth());
  BoolAttr narrow_range = builder.getBoolAttr(false);

  auto add_quantize_op = [&](Location loc, Type input_type, Block* block,
                             Block::iterator insertion_point, Value arg,

        // the asymmetric_quantize_inputs attribute in the builtin option.
        dynamic_range_quantized_user->setAttr(
            kAsymmetricQuantizeInputsAttr,
            BoolAttr::get(rewriter.getContext(), true));
        changed = true;
      }
    }
    return changed;
  }

  // Convert ConstantOp-CastOp-Operation sequence into new ConstantOp

    } else {
      bool disable_call_shape_inference = false;
      if (op->hasAttr("_disable_call_shape_inference")) {
        disable_call_shape_inference =
            op->getAttrOfType<BoolAttr>("_disable_call_shape_inference")
                .getValue();
      }
      inner_op =
          rewriter.create<LegacyCallOp>(loc, new_types, inner_op_operands,

    // Set to `nullptr` because this attribute only matters when the input is
    // dynamic-range quantized.
    const BoolAttr asymmetric_quantize_inputs = nullptr;

    const int lhs_rank = lhs_value.getType().cast<TensorType>().getRank();
    const BoolAttr adj_x =
        (lhs_contracting_dims[0] == lhs_rank - 2 ? rewriter.getBoolAttr(true)

                    const QuantPassSpec& quant_params) {
  rewriter.setInsertionPointAfter(quantized_op);
  FloatAttr tolerance = rewriter.getF32FloatAttr(
      quant_params.numeric_verify_spec.error_tolerance);
  BoolAttr log =
      rewriter.getBoolAttr(quant_params.numeric_verify_spec.log_if_failed_flag);
  // Verify the quantized value by sending the result to the verifier.
  rewriter.create<VerifierT>(

                    Attribute value) {
  ShapedType type;
  if (auto elem_attr = mlir::dyn_cast<ElementsAttr>(value)) {
    return ConstOp::build(builder, result, elem_attr);
  } else if (mlir::isa<BoolAttr, FloatAttr, IntegerAttr>(value)) {
    // All TensorFlow types must be tensor types. In the build() method,
    // we want to provide more flexibility by allowing attributes of scalar