class_docstr: Wrapper for the pyarrow engine for read_csv()
docstr: Reads the contents of a CSV file into a DataFrame and
  processes it according to the kwargs passed in the
  constructor.
  
  Returns
  -------
  DataFrame
      The DataFrame created from the CSV file.
docstr_tok: ["Reads","the","contents","of","a","CSV","file","into","a","DataFrame","and","processes","it","according","to","the","kwargs","passed","in","the","constructor",".","Returns","--","--","--","-","DataFrame","The","DataFrame","created","from","the","CSV","file","."]
code: def read(self) -> DataFrame:
      """
      Reads the contents of a CSV file into a DataFrame and
      processes it according to the kwargs passed in the
      constructor.
  
      Returns
      -------
      DataFrame
          The DataFrame created from the CSV file.
      """
      pa = import_optional_dependency("pyarrow")
      pyarrow_csv = import_optional_dependency("pyarrow.csv")
      self._get_pyarrow_options()
  
      try:
          convert_options = pyarrow_csv.ConvertOptions(
              **self.convert_options
          )
      except TypeError:
          include = self.convert_options.get("include_columns", None)
          if include is not None:
              self._validate_usecols(include)
  
          nulls = self.convert_options.get("null_values", set())
          if not lib.is_list_like(nulls) or not all(
              isinstance(x, str) for x in nulls
          ):
              raise TypeError(
                  "The 'pyarrow' engine requires all na_values to be strings"
              )
  
          raise
  
      try:
          table = pyarrow_csv.read_csv(
              self.src,
              read_options=pyarrow_csv.ReadOptions(**self.read_options),
              parse_options=pyarrow_csv.ParseOptions(
                  **self.parse_options
              ),
              convert_options=convert_options,
          )
      except pa.ArrowInvalid as e:
          raise ParserError(e) from e
  
      dtype_backend = self.kwds["dtype_backend"]
  
      # Convert all pa.null() cols -> float64 (non nullable)
      # else Int64 (nullable case, see below)
      if dtype_backend is lib.no_default:
          new_schema = table.schema
          new_type = pa.float64()
          for i, arrow_type in enumerate(table.schema.types):
              if pa.types.is_null(arrow_type):
                  new_schema = new_schema.set(
                      i, new_schema.field(i).with_type(new_type)
                  )
  
          table = table.cast(new_schema)
  
      if dtype_backend == "pyarrow":
          frame = table.to_pandas(types_mapper=pd.ArrowDtype)
      elif dtype_backend == "numpy_nullable":
          # Modify the default mapping to also
          # map null to Int64 (to match other engines)
          dtype_mapping = _arrow_dtype_mapping()
          dtype_mapping[pa.null()] = pd.Int64Dtype()
          frame = table.to_pandas(types_mapper=dtype_mapping.get)
      elif using_pyarrow_string_dtype():
          frame = table.to_pandas(
              types_mapper=arrow_string_types_mapper()
          )
  
      else:
          frame = table.to_pandas()
      return self._finalize_pandas_output(frame)
  
imports: from __future__ import annotations
  from typing import TYPE_CHECKING
  import warnings
  from pandas._config import using_pyarrow_string_dtype
  from pandas._libs import lib
  from pandas.compat._optional import import_optional_dependency
  from pandas.errors import ParserError, ParserWarning
  from pandas.util._exceptions import find_stack_level
  from pandas.core.dtypes.common import pandas_dtype
  from pandas.core.dtypes.inference import is_integer
  import pandas
  from pandas import DataFrame
  from pandas.io._util import _arrow_dtype_mapping, arrow_string_types_mapper
  from pandas.io.parsers.base_parser import ParserBase
label_desc: Use image node_id 6 for calling the ArrowParserWrapper obj's underlying member method code with example usage: obj.read() and returns: self