code: def rechunk(
      x,
      chunks="auto",
      threshold=None,
      block_size_limit=None,
      balance=False,
      method=None,
  ):
      """
      Convert blocks in dask array x for new chunks.
  
      Parameters
      ----------
      x: dask array
          Array to be rechunked.
      chunks:  int, tuple, dict or str, optional
          The new block dimensions to create. -1 indicates the full size of the
          corresponding dimension. Default is "auto" which automatically
          determines chunk sizes.
      threshold: int, optional
          The graph growth factor under which we don't bother introducing an
          intermediate step.
      block_size_limit: int, optional
          The maximum block size (in bytes) we want to produce
          Defaults to the configuration value ``array.chunk-size``
      balance : bool, default False
          If True, try to make each chunk to be the same size.
  
          This means ``balance=True`` will remove any small leftover chunks, so
          using ``x.rechunk(chunks=len(x) // N, balance=True)``
          will almost certainly result in ``N`` chunks.
      method: {'tasks', 'p2p'}, optional.
          Rechunking method to use.
  
  
      Examples
      --------
      >>> import dask.array as da
      >>> x = da.ones((1000, 1000), chunks=(100, 100))
  
      Specify uniform chunk sizes with a tuple
  
      >>> y = x.rechunk((1000, 10))
  
      Or chunk only specific dimensions with a dictionary
  
      >>> y = x.rechunk({0: 1000})
  
      Use the value ``-1`` to specify that you want a single chunk along a
      dimension or the value ``"auto"`` to specify that dask can freely rechunk a
      dimension to attain blocks of a uniform block size
  
      >>> y = x.rechunk({0: -1, 1: 'auto'}, block_size_limit=1e8)
  
      If a chunk size does not divide the dimension then rechunk will leave any
      unevenness to the last chunk.
  
      >>> x.rechunk(chunks=(400, -1)).chunks
      ((400, 400, 200), (1000,))
  
      However if you want more balanced chunks, and don't mind Dask choosing a
      different chunksize for you then you can use the ``balance=True`` option.
  
      >>> x.rechunk(chunks=(400, -1), balance=True).chunks
      ((500, 500), (1000,))
      """
      # don't rechunk if array is empty
      if x.ndim > 0 and all(s == 0 for s in x.shape):
          return x
  
      if isinstance(chunks, dict):
          chunks = {
              validate_axis(c, x.ndim): v for c, v in chunks.items()
          }
          for i in range(x.ndim):
              if i not in chunks:
                  chunks[i] = x.chunks[i]
              elif chunks[i] is None:
                  chunks[i] = x.chunks[i]
      if isinstance(chunks, (tuple, list)):
          chunks = tuple(
              lc if lc is not None else rc
              for lc, rc in zip(chunks, x.chunks)
          )
      chunks = normalize_chunks(
          chunks,
          x.shape,
          limit=block_size_limit,
          dtype=x.dtype,
          previous_chunks=x.chunks,
      )
  
      # Now chunks are tuple of tuples
      ndim = x.ndim
      if not len(chunks) == ndim:
          raise ValueError(
              "Provided chunks are not consistent with shape"
          )
  
      if not balance and (chunks == x.chunks):
          return x
  
      if balance:
          chunks = tuple(_balance_chunksizes(chunk) for chunk in chunks)
  
      _validate_rechunk(x.chunks, chunks)
  
      method = method or config.get("array.rechunk.method")
  
      if method == "tasks":
          steps = plan_rechunk(
              x.chunks,
              chunks,
              x.dtype.itemsize,
              threshold,
              block_size_limit,
          )
          for c in steps:
              x = _compute_rechunk(x, c)
  
          return x
  
      elif method == "p2p":
          from distributed.shuffle import rechunk_p2p
  
          return rechunk_p2p(x, chunks)
  
      else:
          raise NotImplementedError(
              f"Unknown rechunking method '{method}'"
          )
  
imports: from __future__ import annotations
  import heapq
  import math
  from functools import reduce
  from itertools import chain, count, product
  from operator import add, itemgetter, mul
  from warnings import warn
  import numpy
  import tlz
  from tlz import accumulate
  from dask import config
  from dask.array.chunk import getitem
  from dask.array.core import Array, concatenate3, normalize_chunks
  from dask.array.utils import validate_axis
  from dask.array.wrap import empty
  from dask.base import tokenize
  from dask.highlevelgraph import HighLevelGraph
  from dask.utils import parse_bytes
label_desc: Use image node_id 7 for calling a global function with example usage: rechunk(x, chunks, threshold, block_size_limit, balance, method) and returns: x, x, x, rechunk_p2p