Repository: huggingface/datasets
File: src/datasets/arrow_reader.py (Python)

Code before:
    def _read_files(self, files, in_memory=False) -> Table:
        """Returns Dataset for given file instructions.

        Args:
            files: List[dict(filename, skip, take)], the files information.
                The filenames contain the absolute path, not relative.
                skip/take indicates which example read in the file: `ds.slice(skip, take)`
            in_memory (bool, default False): Whether to copy the data in-memory.
        """
        if len(files) == 0 or not all(isinstance(f, dict) for f in files):
            raise ValueError("please provide valid file informations")
        pa_tables = []
        files = copy.deepcopy(files)
        for f in files:
            f["filename"] = os.path.join(self._path, f["filename"])

        pa_tables = thread_map(
            partial(self._get_table_from_filename, in_memory=in_memory),
            files,
            tqdm_class=hf_tqdm,
            desc="Loading dataset shards",
            disable=len(files) <= 16,
        )
        for f_dict in files:
            pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory)
            pa_tables.append(pa_table)
        pa_tables = [t for t in pa_tables if len(t) > 0]
        if not pa_tables and (self._info is None or self._info.features is None):
            raise ValueError(
                "Tried to read an empty table. Please specify at least info.features to create an empty table with the right type."
            )
        pa_tables = pa_tables or [InMemoryTable.from_batches([], schema=pa.schema(self._info.features.type))]
        pa_table = concat_tables(pa_tables) if len(pa_tables) != 1 else pa_tables[0]
        return pa_table

    def get_file_instructions(self, name, instruction, split_infos):
        """Return list of dict {'filename': str, 'skip': int, 'take': int}"""
        file_instructions = make_file_instructions(
            name, split_infos, instruction, filetype_suffix=self._filetype_suffix, prefix_path=self._path
        )
        files = file_instructions.file_instructions
        return files

    def read(
        self,
        name,
        instructions,
        split_infos,
        in_memory=False,
    ):
        """Returns Dataset instance(s).

Review comment (refactor):
I think you can remove this, and also remove the `pa_tables = []` a few lines above this

```suggestion
```

Code after:
    def _read_files(self, files, in_memory=False) -> Table:
        """Returns Dataset for given file instructions.

        Args:
            files: List[dict(filename, skip, take)], the files information.
                The filenames contain the absolute path, not relative.
                skip/take indicates which example read in the file: `ds.slice(skip, take)`
            in_memory (bool, default False): Whether to copy the data in-memory.
        """
        if len(files) == 0 or not all(isinstance(f, dict) for f in files):
            raise ValueError("please provide valid file informations")
        files = copy.deepcopy(files)
        for f in files:
            f["filename"] = os.path.join(self._path, f["filename"])

        pa_tables = thread_map(
            partial(self._get_table_from_filename, in_memory=in_memory),
            files,
            tqdm_class=hf_tqdm,
            desc="Loading dataset shards",
            disable=len(files) <= 16,
        )
        pa_tables = [t for t in pa_tables if len(t) > 0]
        if not pa_tables and (self._info is None or self._info.features is None):
            raise ValueError(
                "Tried to read an empty table. Please specify at least info.features to create an empty table with the right type."
            )
        pa_tables = pa_tables or [InMemoryTable.from_batches([], schema=pa.schema(self._info.features.type))]
        pa_table = concat_tables(pa_tables) if len(pa_tables) != 1 else pa_tables[0]
        return pa_table

    def get_file_instructions(self, name, instruction, split_infos):
        """Return list of dict {'filename': str, 'skip': int, 'take': int}"""
        file_instructions = make_file_instructions(
            name, split_infos, instruction, filetype_suffix=self._filetype_suffix, prefix_path=self._path
        )
        files = file_instructions.file_instructions
        return files

    def read(
        self,
        name,
        instructions,
        split_infos,
        in_memory=False,
    ):
        """Returns Dataset instance(s).

        Args:
            name (str): name of the dataset.
            instructions (ReadInstruction): instructions to read.