code: def to_sql(
      df,
      name: str,
      uri: str,
      schema=None,
      if_exists: str = "fail",
      index: bool = True,
      index_label=None,
      chunksize=None,
      dtype=None,
      method=None,
      compute=True,
      parallel=False,
      engine_kwargs=None,
  ):
      """Store Dask Dataframe to a SQL table
  
      An empty table is created based on the "meta" DataFrame (and conforming to the caller's "if_exists" preference), and
      then each block calls pd.DataFrame.to_sql (with `if_exists="append"`).
  
      Databases supported by SQLAlchemy [1]_ are supported. Tables can be
      newly created, appended to, or overwritten.
  
      Parameters
      ----------
      name : str
          Name of SQL table.
      uri : string
          Full sqlalchemy URI for the database connection
      schema : str, optional
          Specify the schema (if database flavor supports this). If None, use
          default schema.
      if_exists : {'fail', 'replace', 'append'}, default 'fail'
          How to behave if the table already exists.
  
          * fail: Raise a ValueError.
          * replace: Drop the table before inserting new values.
          * append: Insert new values to the existing table.
  
      index : bool, default True
          Write DataFrame index as a column. Uses `index_label` as the column
          name in the table.
      index_label : str or sequence, default None
          Column label for index column(s). If None is given (default) and
          `index` is True, then the index names are used.
          A sequence should be given if the DataFrame uses MultiIndex.
      chunksize : int, optional
          Specify the number of rows in each batch to be written at a time.
          By default, all rows will be written at once.
      dtype : dict or scalar, optional
          Specifying the datatype for columns. If a dictionary is used, the
          keys should be the column names and the values should be the
          SQLAlchemy types or strings for the sqlite3 legacy mode. If a
          scalar is provided, it will be applied to all columns.
      method : {None, 'multi', callable}, optional
          Controls the SQL insertion clause used:
  
          * None : Uses standard SQL ``INSERT`` clause (one per row).
          * 'multi': Pass multiple values in a single ``INSERT`` clause.
          * callable with signature ``(pd_table, conn, keys, data_iter)``.
  
          Details and a sample callable implementation can be found in the
          section :ref:`insert method <io.sql.method>`.
      compute : bool, default True
          When true, call dask.compute and perform the load into SQL; otherwise, return a Dask object (or array of
          per-block objects when parallel=True)
      parallel : bool, default False
          When true, have each block append itself to the DB table concurrently. This can result in DB rows being in a
          different order than the source DataFrame's corresponding rows. When false, load each block into the SQL DB in
          sequence.
      engine_kwargs : dict or None
          Specific db engine parameters for sqlalchemy
  
      Raises
      ------
      ValueError
          When the table already exists and `if_exists` is 'fail' (the
          default).
  
      See Also
      --------
      read_sql : Read a DataFrame from a table.
  
      Notes
      -----
      Timezone aware datetime columns will be written as
      ``Timestamp with timezone`` type with SQLAlchemy if supported by the
      database. Otherwise, the datetimes will be stored as timezone unaware
      timestamps local to the original timezone.
  
      .. versionadded:: 0.24.0
  
      References
      ----------
      .. [1] https://docs.sqlalchemy.org
      .. [2] https://www.python.org/dev/peps/pep-0249/
  
      Examples
      --------
      Create a table from scratch with 4 rows.
  
      >>> import pandas as pd
      >>> import dask.dataframe as dd
      >>> df = pd.DataFrame([ {'i':i, 's':str(i)*2 } for i in range(4) ])
      >>> ddf = dd.from_pandas(df, npartitions=2)
      >>> ddf  # doctest: +SKIP
      Dask DataFrame Structure:
                         i       s
      npartitions=2
      0              int64  object
      2                ...     ...
      3                ...     ...
      Dask Name: from_pandas, 2 tasks
  
      >>> from dask.utils import tmpfile
      >>> from sqlalchemy import create_engine, text
      >>> with tmpfile() as f:
      ...     db = 'sqlite:///%s' %f
      ...     ddf.to_sql('test', db)
      ...     engine = create_engine(db, echo=False)
      ...     with engine.connect() as conn:
      ...         result = conn.execute(text("SELECT * FROM test")).fetchall()
      >>> result
      [(0, 0, '00'), (1, 1, '11'), (2, 2, '22'), (3, 3, '33')]
      """
      if not isinstance(uri, str):
          raise ValueError(
              f"Expected URI to be a string, got {type(uri)}."
          )
  
      # This is the only argument we add on top of what Pandas supports
      kwargs = dict(
          name=name,
          uri=uri,
          engine_kwargs=engine_kwargs,
          schema=schema,
          if_exists=if_exists,
          index=index,
          index_label=index_label,
          chunksize=chunksize,
          dtype=dtype,
          method=method,
      )
  
      meta_task = delayed(_to_sql_chunk)(df._meta, **kwargs)
  
      # Partitions should always append to the empty table created from `meta` above
      worker_kwargs = dict(kwargs, if_exists="append")
  
      if parallel:
          # Perform the meta insert, then one task that inserts all blocks concurrently:
          result = [
              _extra_deps(
                  _to_sql_chunk,
                  d,
                  extras=meta_task,
                  **worker_kwargs,
                  dask_key_name="to_sql-%s"
                  % tokenize(d, **worker_kwargs),
              )
              for d in df.to_delayed()
          ]
      else:
          # Chain the "meta" insert and each block's insert
          result = []
          last = meta_task
          for d in df.to_delayed():
              result.append(
                  _extra_deps(
                      _to_sql_chunk,
                      d,
                      extras=last,
                      **worker_kwargs,
                      dask_key_name="to_sql-%s"
                      % tokenize(d, **worker_kwargs),
                  )
              )
              last = result[-1]
      result = delayed(result)
  
      if compute:
          dask_compute(result)
      else:
          return result
  
file_path: dask/dask/dataframe/io/sql.py
imports: from __future__ import annotations
  import warnings
  import numpy
  import pandas
  from dask.base import compute
  from dask.dataframe import methods
  from dask.dataframe.io.io import from_delayed, from_pandas
  from dask.dataframe.utils import pyarrow_strings_enabled
  from dask.delayed import delayed, tokenize
  from dask.utils import parse_bytes
label_desc: Use image node_id 6 for calling a global function with example usage: to_sql(df, name, uri, schema, if_exists, index, index_label, chunksize, dtype, method, compute, parallel, engine_kwargs) and returns: result