code: def _flatten_kernel_dict(
      k: dict[str, Any], x2_is_none: bool, is_parallel: bool
  ) -> dict[str, Any]:
      if "nngp" in k:
          # We only use `batch_size` to compute `shape1` and `shape2` for the batch.
          # This only happens if k_dict came from a `Kernel` in which case it must
          # have 'nngp'. I do think there is a failure case if the user called
          # >>> batched_kernel_fn(x1, x2, get=('ntk', 'shape1'))
          # but I don't think this will get hit ever (and certainly before we rework
          # this code).
          batch_size = {
              "1": k["nngp"].shape[0],
              "2": k["nngp"].shape[1],
          }
  
      if "diagonal_batch" in k and not k["diagonal_batch"]:
          raise NotImplementedError(
              "Batching not implemented for "
              "`diagonal_batch == False`."
          )
  
      for key, value in k.items():
          if key == "cov1":
              k[key] = _flatten_batch_dimensions(value, is_parallel, 1)
          elif key == "cov2":
              if x2_is_none:
                  k[key] = None
              else:
                  k[key] = _flatten_batch_dimensions(
                      value, is_parallel, 0
                  )
          elif key == "x1_is_x2":
              k[key] = value[(0,) * value.ndim]
          elif key == "mask1":
              if value is None:
                  k[key] = None
              else:
                  k[key] = _flatten_batch_dimensions(
                      value, is_parallel, 1
                  )
          elif key == "mask2":
              if value is None or x2_is_none:
                  k[key] = None
              else:
                  k[key] = -_flatten_batch_dimensions(
                      value, is_parallel, 0
                  )
          elif key in ("shape1", "shape2"):
              if key == "shape2" and is_parallel:
                  continue
              batch_axis = k["batch_axis"]
              shape = value
              k[key] = (
                  shape[:batch_axis]
                  + (shape[batch_axis] * batch_size[key[-1]],)
                  + shape[batch_axis + 1 :]
              )
          elif isinstance(k[key], (np.ndarray, jnp.ndarray)):
              k[key] = _flatten_batch_dimensions(value, is_parallel)
          else:
              pass
      return k
  
imports: from functools import partial
  from typing import Any, Callable, Iterable, Optional, TypeVar, Union
  import warnings
  import jax
  from jax import device_put
  from jax import devices
  from jax import jit
  from jax import pmap
  from jax import random
  import jax.numpy
  from jax.tree_util import tree_all
  from jax.tree_util import tree_flatten
  from jax.tree_util import tree_map
  from jax.tree_util import tree_unflatten
  import numpy
  from .utils import utils
  from .utils.kernel import Kernel
  from .utils.typing import KernelFn
  from .utils.typing import NTTree
label_desc: Use image node_id 4 for calling a global function with example usage: _flatten_kernel_dict(k, x2_is_none, is_parallel) and returns: k