code: def empty(
      shape: Shape,
      dtype: DTypeLike | None = None,
      index_dtype: DTypeLike = "int32",
      sparse_format: str = "bcoo",
      **kwds,
  ) -> JAXSparse:
      """Create an empty sparse array.
  
      Args:
        shape: sequence of integers giving the array shape.
        dtype: (optional) dtype of the array.
        index_dtype: (optional) dtype of the index arrays.
        format: string specifying the matrix format (e.g. ['bcoo']).
        **kwds: additional keywords passed to the format-specific _empty constructor.
      Returns:
        mat: empty sparse matrix.
      """
      formats = {
          "bcsr": BCSR,
          "bcoo": BCOO,
          "coo": COO,
          "csr": CSR,
          "csc": CSC,
      }
      if sparse_format not in formats:
          raise ValueError(
              f"sparse_format={sparse_format!r} not recognized; "
              f"must be one of {list(formats.keys())}"
          )
      cls = formats[sparse_format]
      return cls._empty(
          shape, dtype=dtype, index_dtype=index_dtype, **kwds
      )
  
imports: from __future__ import annotations
  from functools import partial
  import operator
  from typing import Optional, Union
  import jax
  from jax import tree_util
  from jax.experimental.sparse._base import JAXSparse
  from jax.experimental.sparse.bcoo import BCOO
  from jax.experimental.sparse.bcsr import BCSR
  from jax.experimental.sparse.coo import COO
  from jax.experimental.sparse.csr import CSR, CSC
  from jax.experimental.sparse.util import _coo_extract
  from jax.interpreters import mlir
  from jax._src import core
  from jax._src import dtypes
  from jax._src.interpreters import ad
  from jax._src.interpreters import batching
  from jax._src.typing import Array, DTypeLike, Shape
label_desc: Use image node_id 7 for calling a global function with example usage: empty(shape, dtype, index_dtype, sparse_format) and returns: cls