code: def arrow_to_pydf(
      data: pa.Table,
      schema: SchemaDefinition | None = None,
      *,
      schema_overrides: SchemaDict | None = None,
      rechunk: bool = True,
  ) -> PyDataFrame:
      """Construct a PyDataFrame from an Arrow Table."""
      original_schema = schema
      column_names, schema_overrides = _unpack_schema(
          (schema or data.column_names),
          schema_overrides=schema_overrides,
      )
      try:
          if column_names != data.column_names:
              data = data.rename_columns(column_names)
      except pa.lib.ArrowInvalid as e:
          raise ValueError(
              "dimensions of columns arg must match data dimensions"
          ) from e
  
      data_dict = {}
      # dictionaries cannot be built in different batches (categorical does not allow
      # that) so we rechunk them and create them separately.
      dictionary_cols = {}
      # struct columns don't work properly if they contain multiple chunks.
      struct_cols = {}
      names = []
      for i, column in enumerate(data):
          # extract the name before casting
          name = f"column_{i}" if column._name is None else column._name
          names.append(name)
  
          column = coerce_arrow(column)
          if pa.types.is_dictionary(column.type):
              ps = arrow_to_pyseries(name, column, rechunk=rechunk)
              dictionary_cols[i] = wrap_s(ps)
          elif (
              isinstance(column.type, pa.StructType)
              and column.num_chunks > 1
          ):
              ps = arrow_to_pyseries(name, column, rechunk=rechunk)
              struct_cols[i] = wrap_s(ps)
          else:
              data_dict[name] = column
  
      if len(data_dict) > 0:
          tbl = pa.table(data_dict)
  
          # path for table without rows that keeps datatype
          if tbl.shape[0] == 0:
              pydf = pl.DataFrame(
                  [
                      pl.Series(name, c)
                      for (name, c) in zip(
                          tbl.column_names, tbl.columns
                      )
                  ]
              )._df
          else:
              pydf = PyDataFrame.from_arrow_record_batches(
                  tbl.to_batches()
              )
      else:
          pydf = pl.DataFrame([])._df
      if rechunk:
          pydf = pydf.rechunk()
  
      reset_order = False
      if len(dictionary_cols) > 0:
          df = wrap_df(pydf)
          df = df.with_columns(
              [F.lit(s).alias(s.name) for s in dictionary_cols.values()]
          )
          reset_order = True
  
      if len(struct_cols) > 0:
          df = wrap_df(pydf)
          df = df.with_columns(
              [F.lit(s).alias(s.name) for s in struct_cols.values()]
          )
          reset_order = True
  
      if reset_order:
          df = df[names]
          pydf = df._df
  
      if column_names != original_schema and (
          schema_overrides or original_schema
      ):
          pydf = _post_apply_columns(
              pydf, original_schema, schema_overrides=schema_overrides
          )
      elif schema_overrides:
          for col, dtype in zip(pydf.columns(), pydf.dtypes()):
              override_dtype = schema_overrides.get(col)
              if override_dtype is not None and dtype != override_dtype:
                  pydf = _post_apply_columns(
                      pydf,
                      original_schema,
                      schema_overrides=schema_overrides,
                  )
                  break
  
      return pydf
  
imports: from __future__ import annotations
  import contextlib
  import warnings
  from datetime import date, datetime, time, timedelta
  from decimal import Decimal
  from functools import lru_cache, singledispatch
  from itertools import islice, zip_longest
  from operator import itemgetter
  from sys import version_info
  from typing import TYPE_CHECKING, Any, Callable, Generator, Iterable, Iterator, Mapping, MutableMapping, Sequence, get_type_hints
  import polars._reexport
  from polars import functions
  from polars.datatypes import INTEGER_DTYPES, N_INFER_DEFAULT, TEMPORAL_DTYPES, Boolean, Categorical, Date, Datetime, Duration, Enum, List, Null, Object, String, Struct, Time, UInt32, Unknown, dtype_to_py_type, is_polars_dtype, numpy_char_code_to_dtype, py_type_to_dtype
  from polars.datatypes.constructor import numpy_type_to_constructor, numpy_values_and_dtype, polars_type_to_constructor, py_type_to_constructor
  from polars.dependencies import _NUMPY_AVAILABLE, _check_for_numpy, _check_for_pandas, _check_for_pydantic, dataclasses, pydantic
  from polars.dependencies import numpy
  from polars.dependencies import pandas
  from polars.dependencies import pyarrow
  from polars.exceptions import ComputeError, ShapeError, TimeZoneAwareConstructorWarning
  from polars.utils._wrap import wrap_df, wrap_s
  from polars.utils.meta import get_index_type, threadpool_size
  from polars.utils.various import _is_generator, arrlen, find_stacklevel, parse_version, range_to_series
label_desc: Use image node_id 37 for calling a global function with example usage: arrow_to_pydf(data, schema) and returns: pydf