You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: read_impl.rs
    path of file: ./repos/polars/crates/polars-io/src/parquet
    
    the code of the file until where you have to start completion: use std::borrow::Cow;
use std::collections::VecDeque;
use std::ops::{Deref, Range};

use arrow::array::new_empty_array;
use arrow::datatypes::ArrowSchemaRef;
use polars_core::prelude::*;
use polars_core::utils::{accumulate_dataframes_vertical, split_df};
use polars_core::POOL;
use polars_parquet::read;
use polars_parquet::read::{ArrayIter, FileMetaData, RowGroupMetaData};
use rayon::prelude::*;

use super::materialize_empty_df;
use super::mmap::ColumnStore;
use crate::mmap::{MmapBytesReader, ReaderBytes};
#[cfg(feature = "cloud")]
use crate::parquet::async_impl::FetchRowGroupsFromObjectStore;
use crate::parquet::mmap::mmap_columns;
use crate::parquet::predicates::read_this_row_group;
use crate::parquet::{mmap, FileMetaDataRef, ParallelStrategy};
use crate::predicates::{apply_predicate, PhysicalIoExpr};
use crate::utils::get_reader_bytes;
use crate::RowIndex;

#[cfg(debug_assertions)]
// Ensure we get the proper polars types from schema inference
// This saves unneeded casts.
fn assert_dtypes(data_type: &ArrowDataType) {
    match data_type {
        ArrowDataType::Utf8 => {
            unreachable!()
        },
        ArrowDataType::Binary => {
            unreachable!()
        },
        ArrowDataType::List(_) => {
            unreachable!()
        },
        ArrowDataType::LargeList(inner) => {
            assert_dty
    
    pub async fn next_batches(&mut self, n: usize) -> PolarsResult<Option<Vec<DataFrame>>> {
        if self.limit == 0 && self.has_returned {
            return if self.chunks_fifo.is_empty() {
                Ok(None)
            } else {
                Ok(Some(self.chunks_fifo.drain(..n).collect()))
            };
        }

        let mut skipped_all_rgs = false;
        // fill up fifo stack
        if self.row_group_offset < self.n_row_groups && self.chunks_fifo.len() < n {
            // Ensure we apply the limit on the metadata, before we download the row-groups.
            let row_group_start = self.row_group_offset;
            let row_group_end = compute_row_group_range(
                row_group_start,
                row_group_start + n,
                self.limit,
                &self.metadata.row_groups,
            );

            let store = self
                .row_group_fetcher
                .fetch_row_groups(row_group_start..row_group_end)
                .await?;

            let dfs = rg_to_dfs(
                &store,
                &mut self.rows_read,
                row_group_start,
                row_group_end,
                &mut self.limit,
                &self.metadata,
                &self.schema,
                self.predicate.as_deref(),
                self.row_index.clone(),
                self.parallel,
                &self.projection,
                self.use_statistics,
                self.hive_partition_columns.as_deref(),
            )?;

            self.row_group_offset += n;

            // case where there is no data in the file
            // the streaming engine needs at least a single chunk
            if self.rows_read == 0 && dfs.is_empty() {
                return Ok(Some(vec![materialize_empty_df(
                    Some(&self.projection),
                    self.schema.as_ref(),
                    self.hive_partition_columns.as_deref(),
                    self.row_index.as_ref(),
                )]));
            }

            // TODO! this is slower than it needs to be
            // we also need to parallelize over row groups here.

            skipped_all_rgs |= dfs.is_empty();
            for mut df in dfs {
                // make sure that the chunks are not too large
                let n = df.shape().0 / self.chunk_size;
                if n > 1 {
                    for df in split_df(&mut df, n)? {
                        self.chunks_fifo.push_back(df)
                    }
                } else {
                    self.chunks_fifo.push_back(df)
                }
            }
        };

        if self.chunks_fifo.is_empty() {
            if skipped_all_rgs {
                Ok(Some(vec![materialize_empty_df(
                    Some(self.projection.as_slice()),
                    self.schema(),
                    self.hive_partition_columns.as_deref(),
                    self.row_index.as_ref(),
                )]))
            } else {
                Ok(None)
            }
        } else {
            let mut chunks = Vec::with_capacity(n);
            let mut i = 0;
            while let Some(df) = self.chunks_fifo.pop_front() {
                chunks.push(df);
                i += 1;
                if i == n {
                    break;
                }
            }

            self.has_returned |= true;
            Ok(Some(chunks))
        }
    }