code: def _flatten_batch_dimensions(
      k: jnp.ndarray,
      is_parallel: bool,
      discard_axis: Optional[int] = None,
  ) -> jnp.ndarray:
      """Takes a kernel that has been evaluated in batches and flattens."""
  
      if discard_axis is not None:
          if not is_parallel:
              k = jnp.take(k, 0, axis=discard_axis)
              return jnp.reshape(k, (-1,) + k.shape[2:])
  
          if discard_axis == 1:
              return jnp.reshape(
                  k, (k.shape[0] * k.shape[1],) + k.shape[2:]
              )
  
          return k[0]
  
      else:
          if is_parallel:
              return jnp.reshape(
                  k, (k.shape[0] * k.shape[1],) + k.shape[2:]
              )
  
          k = jnp.transpose(k, (0, 2, 1, 3) + tuple(range(4, k.ndim)))
          return jnp.reshape(
              k,
              (k.shape[0] * k.shape[1], k.shape[2] * k.shape[3])
              + k.shape[4:],
          )
  
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 3 for calling a global function with example usage: _flatten_batch_dimensions(k, is_parallel, discard_axis) and returns: k, jnp, jnp, jnp, jnp