}

/// Returns whether the given `a` and `b` have broadcast-compatible
/// types.
bool IsBroadcastableElementsAttrs(mlir::TypedAttr a, mlir::TypedAttr b);
// Returns true if every dimension of the attribute is 1 except the last one.
bool IsDimensionsDegenerateExceptLastOne(mlir::TypedAttr val);
// Returns true if every element is 1 except the last one.
bool IsDimensionsDegenerateExceptLastOne(ArrayRef<int64_t> elements_shape);

  return !std::any_of(elements.begin(), elements.end(), [](Attribute e) {
    return mlir::cast<IntegerAttr>(e).getValue() != 1;
  });
}

bool IsBroadcastableElementsAttrs(mlir::TypedAttr a, mlir::TypedAttr b) {
  // This would return false if we had unranked tensors (where they should
  // probably be considered as broadcastable), but given we are working with
  // attributes here that shouldn't be an issue,

  if (!rop) return failure();

  llvm::SmallVector<Type, 4> input_specs, out_specs;
  for (auto spec : rop.getInputSpecs()) {
    input_specs.push_back(spec.cast<TypeAttr>().getValue());
  }
  for (auto spec : rop.getOutputSpecs()) {
    out_specs.push_back(spec.cast<TypeAttr>().getValue());
  }

  auto in_spec = input_specs[0].dyn_cast<UniformQuantizedType>();
  // TODO(fengliuai): handles the PerAxis QuantizedType.

  return srcScastOp.getArg();
}

/// The quantization specification should match the expressed type.
static bool isValidQuantizationSpec(Attribute quantSpec, Type expressed) {
  if (auto typeAttr = mlir::dyn_cast<TypeAttr>(quantSpec)) {
    Type spec = typeAttr.getValue();
    if (mlir::isa<TensorType, VectorType>(spec)) return false;

    // The spec should be either a quantized type which is compatible to the

  return srcScastOp.getArg();
}

/// The quantization specification should match the expressed type.
static bool isValidQuantizationSpec(Attribute quantSpec, Type expressed) {
  if (auto typeAttr = mlir::dyn_cast<TypeAttr>(quantSpec)) {
    Type spec = typeAttr.getValue();
    if (mlir::isa<TensorType, VectorType>(spec)) return false;

    // The spec should be either a quantized type which is compatible to the

// folding logic will use a "arith.constant" op to replace the
// "tf.FakeQuantWithMinMaxVarsOp", the "tfl.quantize" op is used to preserve
// the quantization parameters as a TypeAttr and "tfl.dequantize" op used to
// convert the output type to the next op. Here are the transformations:
//
// input   min cst       max cst          input   min cst       max cst

    case AttrValue::kB:
      return builder->getBoolAttr(value.b());
    case AttrValue::kType: {
      mlir::Type type;
      TF_RETURN_IF_ERROR(ConvertDataType(value.type(), *builder, &type));
      return mlir::TypeAttr::get(type);
    }
    case AttrValue::kShape:
      return ConvertTensorShapeProto(value.shape(), builder->getContext());
    case AttrValue::kTensor:
      return ConvertTensorProto(value.tensor(), builder);

// before the op being constant folded. Since the constant
// folding logic will use a "arith.constant" op to replace the
// "tf.FakeQuantWithMinMaxVarsOp", the "quant.qcast" op is used to preserve
// the quantization parameters as a TypeAttr and "quant.dcast" op used to
// convert the output type to the next op. Here are the transformations:
//
// input   min cst       max cst              input
//  \       |             |                     |

// before the op being constant folded. Since the constant
// folding logic will use a "arith.constant" op to replace the
// "tf.FakeQuantWithMinMaxVarsOp", the "tfl.quantize" op is used to preserve
// the quantization parameters as a TypeAttr and "tfl.dequantize" op used to
// convert the output type to the next op. Here are the transformations:
//
// input   min cst       max cst          input   min cst       max cst

          builder.setInsertionPoint(assign_variable_op);
          auto new_q_op = builder.create<QuantizeOp>(
              assign_variable_op.getLoc(), ref_qtype, dq_op.getInput(),
              TypeAttr::get(ref_qtype));
          auto new_assign_variable_op = builder.create<AssignVariableOp>(
              assign_variable_op.getLoc(), assign_variable_op.getResourceId(),
              new_q_op.getResult());