code: def _adaptive_pool1d(
      x: Array, target_size: int, operation: Callable[[Array], Array]
  ) -> Array:
      """**Arguments:**
  
      - `x`: The input. Should be a JAX array of shape `(dim,)`.
      - `target_size`: The shape of the output after the pooling operation
          `(target_size,)`.
      - `operation`: The pooling operation to be performed on the input array.
  
      **Returns:**
  
      A JAX array of shape `(1, target_shape)`.
      """
      dims = jnp.size(x)
      num_head_arrays = dims % target_size
      if num_head_arrays != 0:
          head_end_index = num_head_arrays * (dims // target_size + 1)
          head_op = jax.vmap(operation)(
              x[:head_end_index].reshape(num_head_arrays, -1)
          )
          tail_op = jax.vmap(operation)(
              x[head_end_index:].reshape(-1, dims // target_size)
          )
          outputs = jnp.concatenate([head_op, tail_op])
      else:
          outputs = jax.vmap(operation)(
              jax.vmap(operation)(x.reshape(-1, dims // target_size))
          )
      return outputs
  
imports: import math
  from collections.abc import Callable, Sequence
  from typing import Optional, Union
  import jax
  import jax.lax
  import jax.numpy
  import jax.random
  from jaxtyping import Array, PRNGKeyArray
  from .._module import field, Module
  from ._misc import all_sequences
label_desc: Use image node_id 1 for calling a global function with example usage: _adaptive_pool1d(x, target_size, operation) and returns: outputs