code: def from_map(
      func,
      *iterables,
      args=None,
      meta=None,
      divisions=None,
      label=None,
      token=None,
      enforce_metadata=True,
      **kwargs,
  ):
      """Create a DataFrame collection from a custom function map
  
      WARNING: The ``from_map`` API is experimental, and stability is not
      yet guaranteed. Use at your own risk!
  
      Parameters
      ----------
      func : callable
          Function used to create each partition. If ``func`` satisfies the
          ``DataFrameIOFunction`` protocol, column projection will be enabled.
      *iterables : Iterable objects
          Iterable objects to map to each output partition. All iterables must
          be the same length. This length determines the number of partitions
          in the output collection (only one element of each iterable will
          be passed to ``func`` for each partition).
      args : list or tuple, optional
          Positional arguments to broadcast to each output partition. Note
          that these arguments will always be passed to ``func`` after the
          ``iterables`` positional arguments.
      $META
      divisions : tuple, str, optional
          Partition boundaries along the index.
          For tuple, see https://docs.dask.org/en/latest/dataframe-design.html#partitions
          For string 'sorted' will compute the delayed values to find index
          values.  Assumes that the indexes are mutually sorted.
          If None, then won't use index information
      label : str, optional
          String to use as the function-name label in the output
          collection-key names.
      token : str, optional
          String to use as the "token" in the output collection-key names.
      enforce_metadata : bool, default True
          Whether to enforce at runtime that the structure of the DataFrame
          produced by ``func`` actually matches the structure of ``meta``.
          This will rename and reorder columns for each partition,
          and will raise an error if this doesn't work,
          but it won't raise if dtypes don't match.
      **kwargs:
          Key-word arguments to broadcast to each output partition. These
          same arguments will be passed to ``func`` for every output partition.
  
      Examples
      --------
      >>> import pandas as pd
      >>> import dask.dataframe as dd
      >>> func = lambda x, size=0: pd.Series([x] * size)
      >>> inputs = ["A", "B"]
      >>> dd.from_map(func, inputs, size=2).compute()
      0    A
      1    A
      0    B
      1    B
      dtype: object
  
      This API can also be used as an alternative to other file-based
      IO functions, like ``read_parquet`` (which are already just
      ``from_map`` wrapper functions):
  
      >>> import pandas as pd
      >>> import dask.dataframe as dd
      >>> paths = ["0.parquet", "1.parquet", "2.parquet"]
      >>> dd.from_map(pd.read_parquet, paths).head()  # doctest: +SKIP
                          name
      timestamp
      2000-01-01 00:00:00   Laura
      2000-01-01 00:00:01  Oliver
      2000-01-01 00:00:02   Alice
      2000-01-01 00:00:03  Victor
      2000-01-01 00:00:04     Bob
  
      Since ``from_map`` allows you to map an arbitrary function
      to any number of iterable objects, it can be a very convenient
      means of implementing functionality that may be missing from
      from other DataFrame-creation methods. For example, if you
      happen to have apriori knowledge about the number of rows
      in each of the files in a dataset, you can generate a
      DataFrame collection with a global RangeIndex:
  
      >>> import pandas as pd
      >>> import numpy as np
      >>> import dask.dataframe as dd
      >>> paths = ["0.parquet", "1.parquet", "2.parquet"]
      >>> file_sizes = [86400, 86400, 86400]
      >>> def func(path, row_offset):
      ...     # Read parquet file and set RangeIndex offset
      ...     df = pd.read_parquet(path)
      ...     return df.set_index(
      ...         pd.RangeIndex(row_offset, row_offset+len(df))
      ...     )
      >>> def get_ddf(paths, file_sizes):
      ...     offsets = [0] + list(np.cumsum(file_sizes))
      ...     return dd.from_map(
      ...         func, paths, offsets[:-1], divisions=offsets
      ...     )
      >>> ddf = get_ddf(paths, file_sizes)  # doctest: +SKIP
      >>> ddf.index  # doctest: +SKIP
      Dask Index Structure:
      npartitions=3
      0         int64
      86400       ...
      172800      ...
      259200      ...
      dtype: int64
      Dask Name: myfunc, 6 tasks
  
      See Also
      --------
      dask.dataframe.from_delayed
      dask.layers.DataFrameIOLayer
      """
  
      # Input validation
      if not callable(func):
          raise ValueError("`func` argument must be `callable`")
      lengths = set()
      iterables = list(iterables)
      for i, iterable in enumerate(iterables):
          if not isinstance(iterable, Iterable):
              raise ValueError(
                  f"All elements of `iterables` must be Iterable, got {type(iterable)}"
              )
          try:
              lengths.add(len(iterable))
          except (AttributeError, TypeError):
              iterables[i] = list(iterable)
              lengths.add(len(iterables[i]))
      if len(lengths) == 0:
          raise ValueError(
              "`from_map` requires at least one Iterable input"
          )
      elif len(lengths) > 1:
          raise ValueError("All `iterables` must have the same length")
      if lengths == {0}:
          raise ValueError(
              "All `iterables` must have a non-zero length"
          )
  
      # Check for `produces_tasks` and `creation_info`.
      # These options are included in the function signature,
      # because they are not intended for "public" use.
      produces_tasks = kwargs.pop("produces_tasks", False)
      creation_info = kwargs.pop("creation_info", None)
  
      if produces_tasks or len(iterables) == 1:
          if len(iterables) > 1:
              # Tasks are not detected correctly when they are "packed"
              # within an outer list/tuple
              raise ValueError(
                  "Multiple iterables not supported when produces_tasks=True"
              )
          inputs = iterables[0]
          packed = False
      else:
          inputs = list(zip(*iterables))
          packed = True
  
      # Define collection name
      label = label or funcname(func)
      token = token or tokenize(
          func,
          meta,
          inputs,
          args,
          divisions,
          enforce_metadata,
          **kwargs,
      )
      name = f"{label}-{token}"
  
      # Get "projectable" column selection.
      # Note that this relies on the IO function
      # ducktyping with DataFrameIOFunction
      column_projection = (
          func.columns
          if isinstance(func, DataFrameIOFunction)
          else None
      )
  
      # NOTE: Most of the metadata-handling logic used here
      # is copied directly from `map_partitions`
      if meta is None:
          meta = _emulate(
              func,
              *(inputs[0] if packed else inputs[:1]),
              *(args or []),
              udf=True,
              **kwargs,
          )
          meta_is_emulated = True
      else:
          meta = make_meta(meta)
          meta_is_emulated = False
  
      if not (
          has_parallel_type(meta) or is_arraylike(meta) and meta.shape
      ):
          if not meta_is_emulated:
              raise TypeError(
                  "Meta is not valid, `from_map` expects output to be a pandas object. "
                  "Try passing a pandas object as meta or a dict or tuple representing the "
                  "(name, dtype) of the columns."
              )
          # If `meta` is not a pandas object, the concatenated results will be a
          # different type
          meta = make_meta(_concat([meta]))
  
      # Ensure meta is empty DataFrame
      meta = make_meta(meta)
  
      # Define io_func
      if packed or args or kwargs or enforce_metadata:
          io_func = _PackedArgCallable(
              func,
              args=args,
              kwargs=kwargs,
              meta=meta if enforce_metadata else None,
              enforce_metadata=enforce_metadata,
              packed=packed,
          )
      else:
          io_func = func
  
      # Construct DataFrameIOLayer
      layer = DataFrameIOLayer(
          name,
          column_projection,
          inputs,
          io_func,
          label=label,
          produces_tasks=produces_tasks,
          creation_info=creation_info,
      )
  
      # Return new DataFrame-collection object
      divisions = divisions or [None] * (len(inputs) + 1)
      graph = HighLevelGraph.from_collections(
          name, layer, dependencies=[]
      )
      return new_dd_object(graph, name, meta, divisions)
  
imports: from __future__ import annotations
  from collections.abc import Iterable
  from functools import partial
  from math import ceil
  from operator import getitem
  from threading import Lock
  from typing import TYPE_CHECKING, Literal, overload
  import numpy
  import pandas
  import dask.array
  from dask.base import is_dask_collection, tokenize
  from dask.blockwise import BlockwiseDepDict, blockwise
  from dask.dataframe._compat import is_any_real_numeric_dtype
  from dask.dataframe.backends import dataframe_creation_dispatch
  from dask.dataframe.core import DataFrame, Index, Series, _concat, _emulate, _Frame, apply_and_enforce, has_parallel_type, new_dd_object
  from dask.dataframe.dispatch import meta_lib_from_array
  from dask.dataframe.io.utils import DataFrameIOFunction
  from dask.dataframe.utils import check_meta, insert_meta_param_description, is_series_like, make_meta
  from dask.delayed import Delayed, delayed
  from dask.highlevelgraph import HighLevelGraph
  from dask.layers import DataFrameIOLayer
  from dask.utils import M, funcname, is_arraylike
label_desc: Use image node_id 15 for calling a global function with example usage: from_map(func) and returns: new_dd_object