code: def from_dict(
      data: Mapping[
          str,
          Sequence[object] | Mapping[str, Sequence[object]] | Series,
      ],
      schema: SchemaDefinition | None = None,
      *,
      schema_overrides: SchemaDict | None = None,
  ) -> DataFrame:
      """
      Construct a DataFrame from a dictionary of sequences.
  
      This operation clones data, unless you pass a `{str: pl.Series,}` dict.
  
      Parameters
      ----------
      data : dict of sequences
          Two-dimensional data represented as a dictionary. dict must contain
          Sequences.
      schema : Sequence of str, (str,DataType) pairs, or a {str:DataType,} dict
          The DataFrame schema may be declared in several ways:
  
          * As a dict of {name:type} pairs; if type is None, it will be auto-inferred.
          * As a list of column names; in this case types are automatically inferred.
          * As a list of (name,type) pairs; this is equivalent to the dictionary form.
  
          If you supply a list of column names that does not match the names in the
          underlying data, the names given here will overwrite them. The number
          of names given in the schema should match the underlying data dimensions.
      schema_overrides : dict, default None
          Support type specification or override of one or more columns; note that
          any dtypes inferred from the columns param will be overridden.
  
      Returns
      -------
      DataFrame
  
      Examples
      --------
      >>> df = pl.from_dict({"a": [1, 2], "b": [3, 4]})
      >>> df
      shape: (2, 2)
      ┌─────┬─────┐
      │ a   ┆ b   │
      │ --- ┆ --- │
      │ i64 ┆ i64 │
      ╞═════╪═════╡
      │ 1   ┆ 3   │
      │ 2   ┆ 4   │
      └─────┴─────┘
  
      """
      return pl.DataFrame._from_dict(
          data, schema=schema, schema_overrides=schema_overrides
      )
  
imports: from __future__ import annotations
  import io
  import re
  from itertools import chain, zip_longest
  from typing import TYPE_CHECKING, Any, Iterable, Mapping, Sequence, overload
  import polars._reexport
  from polars import functions
  from polars.datatypes import N_INFER_DEFAULT, Categorical, List, Object, String, Struct
  from polars.dependencies import pandas
  from polars.dependencies import pyarrow
  from polars.exceptions import NoDataError
  from polars.io import read_csv
  from polars.utils.various import _cast_repr_strings_with_schema
label_desc: Use image node_id 1 for calling a global function with example usage: from_dict(data, schema) and returns: pl