code: def make_meta_object(x, index=None):
      """Create an empty pandas object containing the desired metadata.
  
      Parameters
      ----------
      x : dict, tuple, list, pd.Series, pd.DataFrame, pd.Index, dtype, scalar
          To create a DataFrame, provide a `dict` mapping of `{name: dtype}`, or
          an iterable of `(name, dtype)` tuples. To create a `Series`, provide a
          tuple of `(name, dtype)`. If a pandas object, names, dtypes, and index
          should match the desired output. If a dtype or scalar, a scalar of the
          same dtype is returned.
      index :  pd.Index, optional
          Any pandas index to use in the metadata. If none provided, a
          `RangeIndex` will be used.
  
      Examples
      --------
  
      >>> make_meta_object([('a', 'i8'), ('b', 'O')])
      Empty DataFrame
      Columns: [a, b]
      Index: []
      >>> make_meta_object(('a', 'f8'))
      Series([], Name: a, dtype: float64)
      >>> make_meta_object('i8')
      1
      """
  
      if is_arraylike(x) and x.shape:
          return x[:0]
  
      if index is not None:
          index = make_meta_dispatch(index)
  
      if isinstance(x, dict):
          return pd.DataFrame(
              {
                  c: _empty_series(c, d, index=index)
                  for (c, d) in x.items()
              },
              index=index,
          )
      if isinstance(x, tuple) and len(x) == 2:
          return _empty_series(x[0], x[1], index=index)
      elif isinstance(x, Iterable) and not isinstance(x, str):
          if not all(isinstance(i, tuple) and len(i) == 2 for i in x):
              raise ValueError(
                  f"Expected iterable of tuples of (name, dtype), got {x}"
              )
          return pd.DataFrame(
              {c: _empty_series(c, d, index=index) for (c, d) in x},
              columns=[c for c, d in x],
              index=index,
          )
      elif not hasattr(x, "dtype") and x is not None:
          # could be a string, a dtype object, or a python type. Skip `None`,
          # because it is implictly converted to `dtype('f8')`, which we don't
          # want here.
          try:
              dtype = np.dtype(x)
              return _scalar_from_dtype(dtype)
          except Exception:
              # Continue on to next check
              pass
  
      if is_scalar(x):
          return _nonempty_scalar(x)
  
      raise TypeError(f"Don't know how to create metadata from {x}")
  
imports: from __future__ import annotations
  import warnings
  from collections.abc import Iterable
  import numpy
  import pandas
  from pandas.api.types import is_scalar, union_categoricals
  from dask.array.core import Array
  from dask.array.dispatch import percentile_lookup
  from dask.array.percentile import _percentile
  from dask.backends import CreationDispatch, DaskBackendEntrypoint
  from dask.dataframe._compat import is_any_real_numeric_dtype
  from dask.dataframe.core import DataFrame, Index, Scalar, Series, _Frame
  from dask.dataframe.dispatch import categorical_dtype_dispatch, concat, concat_dispatch, from_pyarrow_table_dispatch, get_parallel_type, group_split_dispatch, grouper_dispatch, hash_object_dispatch, is_categorical_dtype_dispatch, make_meta_dispatch, make_meta_obj, meta_lib_from_array, meta_nonempty, partd_encode_dispatch, pyarrow_schema_dispatch, to_pandas_dispatch, to_pyarrow_table_dispatch, tolist_dispatch, union_categoricals_dispatch
  from dask.dataframe.extensions import make_array_nonempty, make_scalar
  from dask.dataframe.utils import _empty_series, _nonempty_scalar, _scalar_from_dtype, is_float_na_dtype, is_integer_na_dtype
  from dask.sizeof import SimpleSizeof, sizeof
  from dask.utils import is_arraylike, is_series_like, typename
label_desc: Use image node_id 10 for calling a global function with example usage: make_meta_object(x, index) and returns: x, pd, _empty_series, _nonempty_scalar, pd, _scalar_from_dtype