}

/// 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.

      /*asymmetric_quantize_inputs=*/mlir::BoolAttr(),
      /*input_to_input_intermediate=*/mlir::TypeAttr(),
      /*input_to_forget_intermediate=*/mlir::TypeAttr(),
      /*input_to_cell_intermediate=*/mlir::TypeAttr(),
      /*input_to_output_intermediate=*/mlir::TypeAttr(),
      /*effective_hidden_scale_intermediate=*/mlir::TypeAttr());

  // Cast the static shaped lstm result to FuncOp's signature -

        input_specs.push_back(original_input_specs[i]);
      } else if (requantize.pos == RequantizeState::ON_OUTPUT) {
        input_specs.push_back(TypeAttr::get(requantize.params));
      } else {
        input_specs.push_back(TypeAttr::get(state.params));
      }
    }
    op->setAttr("input_specs", ArrayAttr::get(context, input_specs));

    llvm::SmallVector<Attribute, 4> output_specs;

  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

  DataType dtype;
  TF_RETURN_IF_ERROR(ConvertToDataType(arg_type.getElementType(), &dtype));
  AttrValue type_attr;
  type_attr.set_type(dtype);
  (*node_def->mutable_attr())["T"] = type_attr;

  AttrValue index_attr;
  index_attr.set_i(index);
  (*node_def->mutable_attr())["index"] = index_attr;

  if (auto device_attr =

  DataType dtype;
  TF_RETURN_IF_ERROR(ConvertToDataType(arg_type.getElementType(), &dtype));
  AttrValue type_attr;
  type_attr.set_type(dtype);
  (*node_def->mutable_attr())["T"] = type_attr;

  AttrValue index_attr;
  index_attr.set_i(index);
  (*node_def->mutable_attr())["index"] = index_attr;

  if (auto device_attr =

// index.
template <typename LstmOp>
inline QuantizedType GetIntermediateElementType(LstmOp op, int tensor_index) {
  if (tensor_index < 0 || tensor_index > 4) return nullptr;
  TypeAttr attr = op->template getAttrOfType<TypeAttr>(
      intermediate_attributes[tensor_index]);
  if (!attr) {
    return nullptr;
  }
  return QuantizedType::getQuantizedElementType(attr.getValue());
}