code: def avg_pool(
      inputs,
      window_shape,
      strides=None,
      padding="VALID",
      count_include_pad=True,
  ):
      """Pools the input by taking the average over a window.
  
      Args:
        inputs: input data with dimensions (batch, window dims..., features).
        window_shape: a shape tuple defining the window to reduce over.
        strides: a sequence of ``n`` integers, representing the inter-window
          strides (default: ``(1, ..., 1)``).
        padding: either the string ``'SAME'``, the string ``'VALID'``, or a sequence
          of ``n`` ``(low, high)`` integer pairs that give the padding to apply before
          and after each spatial dimension (default: ``'VALID'``).
        count_include_pad: a boolean whether to include padded tokens
          in the average calculation (default: ``True``).
      Returns:
        The average for each window slice.
      """
      y = pool(inputs, 0.0, lax.add, window_shape, strides, padding)
      if count_include_pad:
          y = y / np.prod(window_shape)
      else:
          div_shape = inputs.shape[:-1] + (1,)
          if len(div_shape) - 2 == len(window_shape):
              div_shape = (1,) + div_shape[1:]
          y = y / pool(
              jnp.ones(div_shape),
              0.0,
              lax.add,
              window_shape,
              strides,
              padding,
          )
      return y
  
imports: import jax.numpy
  import numpy
  from jax import lax
label_desc: Use image node_id 2 for calling a global function with example usage: avg_pool(inputs, window_shape, strides, padding, count_include_pad) and returns: y