EXPECT_EQ(0, TF_OperationNumControlOutputs(add));

  ASSERT_TRUE(GetAttrValue(add, "T", &attr_value, s)) << TF_Message(s);
  EXPECT_EQ(attr_value.type(), tensorflow::DT_INT32);
  ASSERT_TRUE(GetAttrValue(add, "N", &attr_value, s)) << TF_Message(s);
  EXPECT_EQ(attr_value.i(), 2);

  // Placeholder oper now has a consumer.
  ASSERT_EQ(1, TF_OperationOutputNumConsumers(TF_Output{feed, 0}));

                              const char* attr_name) {
  bool out = false;
  bool attr_value;
  if (TryGetNodeAttr(node->attrs(), attr_name, &attr_value)) {
    out |= attr_value;
  }

  if (flib_def->GetAttr(*node, attr_name, &attr_value).ok()) {
    out |= attr_value;
  }
  return out;
}

Status MarkForCompilationPassImpl::BuildInitialClusterSet() {

                        const char* attr_name, const char* attr_value,
                        TF_Operation** op) {
  TF_OperationDescription* desc = TF_NewOperation(graph, "Placeholder", name);
  TF_SetAttrType(desc, "dtype", TF_INT32);
  TF_SetAttrString(desc, attr_name, attr_value, strlen(attr_value));
  *op = TF_FinishOperation(desc, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  def.add_input("input1");
  def.add_input("input2");
  def.add_input("input3");
  def.add_input("input3");
  def.add_input("input3");

  AttrValue v0;
  v0.set_type(DataType::DT_INT32);
  v0.set_i(3);
  (*def.mutable_attr())["NumInput3"] = v0;
  AttrValue v1;
  v1.set_type(DataType::DT_FLOAT);
  (*def.mutable_attr())["SomeDataTypeAttr"] = v1;

  tensorflow::AttrValueMap attr_values = ExtractAttrs(minOp);
  tensorflow::AttrValueMap::const_iterator attr_found = attr_values.find("T");
  EXPECT_NE(attr_found, attr_values.cend());
  EXPECT_EQ(attr_found->second.type(), tensorflow::DataType::DT_FLOAT);
  attr_found = attr_values.find("Tidx");
  EXPECT_NE(attr_found, attr_values.cend());

      if attr_val.type == types_pb2.DT_FLOAT:
        return 'f32'
      elif attr_val.type == types_pb2.DT_INT32:
        return 'i32'
      elif attr_val.type == types_pb2.DT_INT64:
        return 'i64'
      elif attr_val.type == types_pb2.DT_BOOL:
        return 'i1'
    # list
    if attr_val.HasField('list'):
      if attr_val.list.f:
        elt_ty = TFRTypes.F32
        values = attr_val.list.f

      `attr_name` is given, check if the `attr_val` matches with the attribute
      value of the op.
    """
    # Check the main graph
    if self._contains_op_with_name_and_attribute(
        nodes=graphdef.node,
        op_name=op_name,
        attr_name=attr_name,
        attr_val=attr_val,
        node_name=node_name,
    ):
      return True

            if (attr == null) {
                continue;
            }

            for (int i = 0; i < attr.size(); i++) {
                final Object attrValue = attr.get(i);
                if (attrValue != null) {
                    final String entryDn = attrValue.toString();

                    if (logger.isDebugEnabled()) {
                        logger.debug("entryDn: {}", entryDn);
                    }

                if (leftOp.equals("org") || leftOp.equals("organization")) {
                    leftOp = "organisation";
                }

                String attrValue = dd.getAttribute(leftOp);
                if (!rightOp.equals(attrValue)) {
                    return null;
                }
            } else {
                String leftOp = condition.substring(0, notEqualIndex).trim();

                                                TF_Status* status);

// `proto` should point to a sequence of bytes of length `proto_len`
// representing a binary serialization of an AttrValue protocol
// buffer.
TF_CAPI_EXPORT extern void TF_SetAttrValueProto(TF_OperationDescription* desc,
                                                const char* attr_name,