code: def reshape(x, shape, merge_chunks=True, limit=None):
      """Reshape array to new shape
  
      Parameters
      ----------
      shape : int or tuple of ints
          The new shape should be compatible with the original shape. If
          an integer, then the result will be a 1-D array of that length.
          One shape dimension can be -1. In this case, the value is
          inferred from the length of the array and remaining dimensions.
      merge_chunks : bool, default True
          Whether to merge chunks using the logic in :meth:`dask.array.rechunk`
          when communication is necessary given the input array chunking and
          the output shape. With ``merge_chunks==False``, the input array will
          be rechunked to a chunksize of 1, which can create very many tasks.
      limit: int (optional)
          The maximum block size to target in bytes. If no limit is provided,
          it defaults to using the ``array.chunk-size`` Dask config value.
  
      Notes
      -----
      This is a parallelized version of the ``np.reshape`` function with the
      following limitations:
  
      1.  It assumes that the array is stored in `row-major order`_
      2.  It only allows for reshapings that collapse or merge dimensions like
          ``(1, 2, 3, 4) -> (1, 6, 4)`` or ``(64,) -> (4, 4, 4)``
  
      .. _`row-major order`: https://en.wikipedia.org/wiki/Row-_and_column-major_order
  
      When communication is necessary this algorithm depends on the logic within
      rechunk.  It endeavors to keep chunk sizes roughly the same when possible.
  
      See :ref:`array-chunks.reshaping` for a discussion the tradeoffs of
      ``merge_chunks``.
  
      See Also
      --------
      dask.array.rechunk
      numpy.reshape
      """
      # Sanitize inputs, look for -1 in shape
      from dask.array.core import PerformanceWarning
      from dask.array.slicing import sanitize_index
  
      shape = tuple(map(sanitize_index, shape))
      known_sizes = [s for s in shape if s != -1]
      if len(known_sizes) < len(shape):
          if len(shape) - len(known_sizes) > 1:
              raise ValueError("can only specify one unknown dimension")
          # Fastpath for x.reshape(-1) on 1D arrays, allows unknown shape in x
          # for this case only.
          if len(shape) == 1 and x.ndim == 1:
              return x
          missing_size = sanitize_index(
              x.size / reduce(mul, known_sizes, 1)
          )
          shape = tuple(missing_size if s == -1 else s for s in shape)
  
      if np.isnan(sum(x.shape)):
          raise ValueError(
              "Array chunk size or shape is unknown. shape: %s\n\n"
              "Possible solution with x.compute_chunk_sizes()"
              % str(x.shape)
          )
  
      if reduce(mul, shape, 1) != x.size:
          raise ValueError("total size of new array must be unchanged")
  
      if x.shape == shape:
          return x
  
      meta = meta_from_array(x, len(shape))
  
      name = "reshape-" + tokenize(x, shape)
  
      if x.npartitions == 1:
          key = next(flatten(x.__dask_keys__()))
          dsk = {(name,) + (0,) * len(shape): (M.reshape, key, shape)}
          chunks = tuple((d,) for d in shape)
          graph = HighLevelGraph.from_collections(
              name, dsk, dependencies=[x]
          )
          return Array(graph, name, chunks, meta=meta)
  
      # Logic or how to rechunk
      din = len(x.shape)
      dout = len(shape)
      if not merge_chunks and din > dout:
          x = x.rechunk({i: 1 for i in range(din - dout)})
  
      inchunks, outchunks = reshape_rechunk(x.shape, shape, x.chunks)
      # Check output chunks are not too large
      max_chunksize_in_bytes = (
          reduce(mul, [max(i) for i in outchunks]) * x.dtype.itemsize
      )
  
      if limit is None:
          limit = parse_bytes(config.get("array.chunk-size"))
          split = config.get("array.slicing.split-large-chunks", None)
      else:
          limit = parse_bytes(limit)
          split = True
  
      if max_chunksize_in_bytes > limit:
          if split is None:
              msg = (
                  "Reshaping is producing a large chunk. To accept the large\n"
                  "chunk and silence this warning, set the option\n"
                  "    >>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):\n"
                  "    ...     array.reshape(shape)\n\n"
                  "To avoid creating the large chunks, set the option\n"
                  "    >>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):\n"
                  "    ...     array.reshape(shape)"
                  "Explictly passing ``limit`` to ``reshape`` will also silence this warning\n"
                  "    >>> array.reshape(shape, limit='128 MiB')"
              )
              warnings.warn(msg, PerformanceWarning, stacklevel=6)
          elif split:
              # Leave chunk sizes unaltered where possible
              matching_chunks = Counter(inchunks) & Counter(outchunks)
              chunk_plan = []
              for out in outchunks:
                  if matching_chunks[out] > 0:
                      chunk_plan.append(out)
                      matching_chunks[out] -= 1
                  else:
                      chunk_plan.append("auto")
              outchunks = normalize_chunks(
                  chunk_plan,
                  shape=shape,
                  limit=limit,
                  dtype=x.dtype,
                  previous_chunks=inchunks,
              )
  
      x2 = x.rechunk(inchunks)
  
      # Construct graph
      in_keys = list(
          product([x2.name], *[range(len(c)) for c in inchunks])
      )
      out_keys = list(
          product([name], *[range(len(c)) for c in outchunks])
      )
      shapes = list(product(*outchunks))
      dsk = {
          a: (M.reshape, b, shape)
          for a, b, shape in zip(out_keys, in_keys, shapes)
      }
  
      graph = HighLevelGraph.from_collections(
          name, dsk, dependencies=[x2]
      )
      return Array(graph, name, outchunks, meta=meta)
  
file_path: dask/dask/array/reshape.py
imports: from __future__ import annotations
  import math
  import warnings
  from collections import Counter
  from functools import reduce
  from itertools import product
  from operator import mul
  import numpy
  from dask import config
  from dask.array.core import Array, normalize_chunks
  from dask.array.utils import meta_from_array
  from dask.base import tokenize
  from dask.core import flatten
  from dask.highlevelgraph import HighLevelGraph
  from dask.utils import M, parse_bytes
label_desc: Use image node_id 4 for calling a global function with example usage: reshape(x, shape, merge_chunks, limit) and returns: Array, x, Array, x