Status Init()

in tensorflow_io/core/kernels/csv_kernels.cc [34:132]
94 lines of code
30 McCabe index (conditional complexity)

  Status Init(const std::vector<string>& input,
              const std::vector<string>& metadata, const void* memory_data,
              const int64 memory_size) override {
    if (input.size() > 1) {
      return errors::InvalidArgument("more than 1 filename is not supported");
    }
    const string& filename = input[0];
    file_.reset(
        new SizedRandomAccessFile(env_, filename, memory_data, memory_size));
    TF_RETURN_IF_ERROR(file_->GetFileSize(&file_size_));

    csv_file_.reset(new ArrowRandomAccessFile(file_.get(), file_size_));

    auto result = ::arrow::csv::TableReader::Make(
        ::arrow::default_memory_pool(), ::arrow::io::default_io_context(),
        csv_file_, ::arrow::csv::ReadOptions::Defaults(),
        ::arrow::csv::ParseOptions::Defaults(),
        ::arrow::csv::ConvertOptions::Defaults());
    if (!result.status().ok()) {
      return errors::InvalidArgument("unable to make a TableReader: ",
                                     result.status());
    }
    reader_ = std::move(result).ValueUnsafe();

    {
      auto result = reader_->Read();
      if (!result.status().ok()) {
        return errors::InvalidArgument("unable to read table: ",
                                       result.status());
      }
      table_ = std::move(result).ValueUnsafe();
    }

    for (int i = 0; i < table_->num_columns(); i++) {
      ::tensorflow::DataType dtype;
      switch (table_->column(i)->type()->id()) {
        case ::arrow::Type::BOOL:
          dtype = ::tensorflow::DT_BOOL;
          break;
        case ::arrow::Type::UINT8:
          dtype = ::tensorflow::DT_UINT8;
          break;
        case ::arrow::Type::INT8:
          dtype = ::tensorflow::DT_INT8;
          break;
        case ::arrow::Type::UINT16:
          dtype = ::tensorflow::DT_UINT16;
          break;
        case ::arrow::Type::INT16:
          dtype = ::tensorflow::DT_INT16;
          break;
        case ::arrow::Type::UINT32:
          dtype = ::tensorflow::DT_UINT32;
          break;
        case ::arrow::Type::INT32:
          dtype = ::tensorflow::DT_INT32;
          break;
        case ::arrow::Type::UINT64:
          dtype = ::tensorflow::DT_UINT64;
          break;
        case ::arrow::Type::INT64:
          dtype = ::tensorflow::DT_INT64;
          break;
        case ::arrow::Type::HALF_FLOAT:
          dtype = ::tensorflow::DT_HALF;
          break;
        case ::arrow::Type::FLOAT:
          dtype = ::tensorflow::DT_FLOAT;
          break;
        case ::arrow::Type::DOUBLE:
          dtype = ::tensorflow::DT_DOUBLE;
          break;
        case ::arrow::Type::STRING:
          dtype = ::tensorflow::DT_STRING;
          break;
        case ::arrow::Type::BINARY:
        case ::arrow::Type::FIXED_SIZE_BINARY:
        case ::arrow::Type::DATE32:
        case ::arrow::Type::DATE64:
        case ::arrow::Type::TIMESTAMP:
        case ::arrow::Type::TIME32:
        case ::arrow::Type::TIME64:
        case ::arrow::Type::DECIMAL:
        case ::arrow::Type::LIST:
        case ::arrow::Type::STRUCT:
        case ::arrow::Type::DICTIONARY:
        case ::arrow::Type::MAP:
        default:
          return errors::InvalidArgument("arrow data type is not supported: ",
                                         table_->column(i)->type()->ToString());
      }
      shapes_.push_back(TensorShape({static_cast<int64>(table_->num_rows())}));
      dtypes_.push_back(dtype);
      columns_.push_back(table_->ColumnNames()[i]);
      columns_index_[table_->ColumnNames()[i]] = i;
    }

    return Status::OK();
  }