code: def sanitize_array(
      data,
      index: Index | None,
      dtype: DtypeObj | None = None,
      copy: bool = False,
      *,
      allow_2d: bool = False,
  ) -> ArrayLike:
      """
      Sanitize input data to an ndarray or ExtensionArray, copy if specified,
      coerce to the dtype if specified.
  
      Parameters
      ----------
      data : Any
      index : Index or None, default None
      dtype : np.dtype, ExtensionDtype, or None, default None
      copy : bool, default False
      allow_2d : bool, default False
          If False, raise if we have a 2D Arraylike.
  
      Returns
      -------
      np.ndarray or ExtensionArray
      """
      if isinstance(data, ma.MaskedArray):
          data = sanitize_masked_array(data)
  
      if isinstance(dtype, PandasDtype):
          # Avoid ending up with a PandasArray
          dtype = dtype.numpy_dtype
  
      # extract ndarray or ExtensionArray, ensure we have no PandasArray
      data = extract_array(data, extract_numpy=True, extract_range=True)
  
      if isinstance(data, np.ndarray) and data.ndim == 0:
          if dtype is None:
              dtype = data.dtype
          data = lib.item_from_zerodim(data)
      elif isinstance(data, range):
          # GH#16804
          data = range_to_ndarray(data)
          copy = False
  
      if not is_list_like(data):
          if index is None:
              raise ValueError(
                  "index must be specified when data is not list-like"
              )
          data = construct_1d_arraylike_from_scalar(
              data, len(index), dtype
          )
          return data
  
      elif isinstance(data, ABCExtensionArray):
          # it is already ensured above this is not a PandasArray
          # Until GH#49309 is fixed this check needs to come before the
          #  ExtensionDtype check
          if dtype is not None:
              subarr = data.astype(dtype, copy=copy)
          elif copy:
              subarr = data.copy()
          else:
              subarr = data
  
      elif isinstance(dtype, ExtensionDtype):
          # create an extension array from its dtype
          _sanitize_non_ordered(data)
          cls = dtype.construct_array_type()
          subarr = cls._from_sequence(data, dtype=dtype, copy=copy)
  
      # GH#846
      elif isinstance(data, np.ndarray):
          if isinstance(data, np.matrix):
              data = data.A
  
          if dtype is None:
              subarr = data
              if data.dtype == object:
                  subarr = maybe_infer_to_datetimelike(data)
  
              if subarr is data and copy:
                  subarr = subarr.copy()
  
          else:
              # we will try to copy by-definition here
              subarr = _try_cast(data, dtype, copy)
  
      elif hasattr(data, "__array__"):
          # e.g. dask array GH#38645
          data = np.array(data, copy=copy)
          return sanitize_array(
              data,
              index=index,
              dtype=dtype,
              copy=False,
              allow_2d=allow_2d,
          )
  
      else:
          _sanitize_non_ordered(data)
          # materialize e.g. generators, convert e.g. tuples, abc.ValueView
          data = list(data)
  
          if len(data) == 0 and dtype is None:
              # We default to float64, matching numpy
              subarr = np.array([], dtype=np.float64)
  
          elif dtype is not None:
              subarr = _try_cast(data, dtype, copy)
  
          else:
              subarr = maybe_convert_platform(data)
              if subarr.dtype == object:
                  subarr = cast(np.ndarray, subarr)
                  subarr = maybe_infer_to_datetimelike(subarr)
  
      subarr = _sanitize_ndim(
          subarr, data, dtype, index, allow_2d=allow_2d
      )
  
      if isinstance(subarr, np.ndarray):
          # at this point we should have dtype be None or subarr.dtype == dtype
          dtype = cast(np.dtype, dtype)
          subarr = _sanitize_str_dtypes(subarr, data, dtype, copy)
  
      return subarr
  
imports: from __future__ import annotations
  from typing import TYPE_CHECKING, Optional, Sequence, Union, cast, overload
  import numpy
  from numpy import ma
  from pandas._libs import lib
  from pandas._libs.tslibs.period import Period
  from pandas._typing import AnyArrayLike, ArrayLike, Dtype, DtypeObj, T
  from pandas.core.dtypes.base import ExtensionDtype, _registry
  from pandas.core.dtypes.cast import construct_1d_arraylike_from_scalar, construct_1d_object_array_from_listlike, maybe_cast_to_datetime, maybe_cast_to_integer_array, maybe_convert_platform, maybe_infer_to_datetimelike, maybe_promote
  from pandas.core.dtypes.common import is_datetime64_ns_dtype, is_dtype_equal, is_extension_array_dtype, is_integer_dtype, is_list_like, is_object_dtype, is_timedelta64_ns_dtype
  from pandas.core.dtypes.dtypes import PandasDtype
  from pandas.core.dtypes.generic import ABCDataFrame, ABCExtensionArray, ABCIndex, ABCPandasArray, ABCRangeIndex, ABCSeries
  from pandas.core.dtypes.missing import isna
  import pandas.core.common
label_desc: Use image node_id 7 for calling a global function with example usage: sanitize_array(data, index, dtype, copy) and returns: subarr, data, sanitize_array