def _visit_tfr_builtins(self, op_name, args, node):
    arg_strs = []
    arg_tys = []
    for arg in args:
      value, ty = self.visit(arg)
      arg_strs.append(value)
      arg_tys.append(ty)
    tfr_op_name = 'tfr.' + op_name[5:]
    ret_tys = (
        TFR_BUILTINS[op_name](*arg_tys)
        if callable(TFR_BUILTINS[op_name]) else TFR_BUILTINS[op_name])

}
}  // namespace

Status GradientRegistry::Register(
    const string& op_name, GradientFunctionFactory gradient_function_factory) {
  auto iter = registry_.find(op_name);
  if (iter != registry_.end()) {
    const string error_msg = "Gradient already exists for op: " + op_name + ".";
    return errors::AlreadyExists(error_msg);
  }
  registry_.insert({op_name, gradient_function_factory});
  return absl::OkStatus();
}

def _find_op(
    graph: ops.Graph, op_name: Optional[str]
) -> Optional[ops.Operation]:
  """Finds the operation with `op_name`.

  Args:
    graph: The graph to find from.
    op_name: Name of the node.

  Returns:
    The operation that corresponds to `op_name`. Returns None iff op_name is an
    empty string or None.

  Raises:
    ValueError: `op_name` is malformed.
  """
  if not op_name:

    const std::string& op_name, const Location& location) {
  using tflite::metrics::ConverterErrorData;
  ConverterErrorData error;
  if (!pass_name.empty()) {
    error.set_subcomponent(pass_name);
  }

  if (!error_message.empty()) {
    error.set_error_message(error_message);
  }

  if (!op_name.empty()) {
    error.mutable_operator_()->set_name(op_name);
  }

  }

  // The quantization parameters for a named tensor.
  message QuantParams {
    // The tensor name has the following convention:
    //     tensor_name := op_name | op_name  ’:’  port_number.
    // If the op has only one port, op_name can be used.
    // If the op has internal states, such as fused LSTM, the port_number should
    // follow a predefined convention.
    oneof name_oneof {
      string name = 1;

absl::StatusOr<bool> IsSmallConstant(Node* n) {
  if (!n->IsConstant()) {
    return false;
  }

  return IsConstantSmall(n);
}

bool IsInPlaceOp(absl::string_view op_name) {
  return op_name == "InplaceUpdate" || op_name == "InplaceAdd" ||
         op_name == "InplaceSub";
}
}  // namespace

Status CloneConstantsForBetterClusteringPassImpl::CloneSmallConstantInputs(
    const absl::flat_hash_set<string>& name_set, Node* n) {

# TensorFlow Op CodeGen Machinery (Experimental)

## Usage

```
usage: generate_cpp  [flags]  OpName1 [OpName2 ...]
Flags:
    --help=false                        bool    Print this help message.
    --category=""                       string  Category for generated ops (e.g. 'math', 'array').
    --namespace=""                      string  Compact C++ namespace, default is 'tensorflow::ops'.

      return true;
    }
  }
  return false;
}

// Returns whether the current op is not supported by the TF Lite runtime.
static bool IsUnsupportedFlexOp(const std::string& op_name) {
  return op_name == "PartitionedCall" || op_name == "StatefulPartitionedCall";
}

// Create description of operation that could not be converted.
static std::string GetOpDescriptionForDebug(Operation* inst) {

       parser.parseAttribute(device, "device", result.attributes) ||
       parser.parseRParen()))
    return mlir::failure();

  if (options.has_op_name &&
      parser.parseAttribute(op_name, "op_name", result.attributes))
    return mlir::failure();

  if (options.has_symbol_ref &&
      parser.parseAttribute(f, "f", result.attributes))
    return mlir::failure();

    return "";
  }
  return std::string(entry->second);
}

string WriteClassFuzzDef(const OpInfo& op_info) {
  string class_signature_str = absl::Substitute(
      "class Fuzz$0 : public FuzzSession<$1> {\n", op_info.op_name,
      absl::StrJoin(op_info.graph_op_def.input_arg(), ", ",
                    [](string* out, const auto arg) {
                      strings::StrAppend(out, "Tensor");