code: def from_dict(
      data,
      npartitions,
      orient="columns",
      dtype=None,
      columns=None,
      constructor=pd.DataFrame,
  ):
      """
      Construct a Dask DataFrame from a Python Dictionary
  
      Parameters
      ----------
      data : dict
          Of the form {field : array-like} or {field : dict}.
      npartitions : int
          The number of partitions of the index to create. Note that depending on
          the size and index of the dataframe, the output may have fewer
          partitions than requested.
      orient : {'columns', 'index', 'tight'}, default 'columns'
          The "orientation" of the data. If the keys of the passed dict
          should be the columns of the resulting DataFrame, pass 'columns'
          (default). Otherwise if the keys should be rows, pass 'index'.
          If 'tight', assume a dict with keys
          ['index', 'columns', 'data', 'index_names', 'column_names'].
      dtype: bool
          Data type to force, otherwise infer.
      columns: string, optional
          Column labels to use when ``orient='index'``. Raises a ValueError
          if used with ``orient='columns'`` or ``orient='tight'``.
      constructor: class, default pd.DataFrame
          Class with which ``from_dict`` should be called with.
  
      Examples
      --------
      >>> import dask.dataframe as dd
      >>> ddf = dd.from_dict({"num1": [1, 2, 3, 4], "num2": [7, 8, 9, 10]}, npartitions=2)
      """
  
      collection_types = {
          type(v) for v in data.values() if is_dask_collection(v)
      }
      if collection_types:
          raise NotImplementedError(
              "from_dict doesn't currently support Dask collections as inputs. "
              f"Objects of type {collection_types} were given in the input dict."
          )
  
      return from_pandas(
          constructor.from_dict(data, orient, dtype, columns),
          npartitions,
      )
  
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 6 for calling a global function with example usage: from_dict(data, npartitions, orient, dtype, columns, constructor) and returns: from_pandas