code: def _overlap_and_add(x: Array, step_size: int) -> Array:
      """Utility function compatible with tf.signal.overlap_and_add.
  
      Args:
        x: An array with `(..., frames, frame_length)`-shape.
        step_size: An integer denoting overlap offsets. Must be less than
          `frame_length`.
  
      Returns:
        An array with `(..., output_size)`-shape containing overlapped signal.
      """
      check_arraylike("_overlap_and_add", x)
      step_size = jax.core.concrete_or_error(
          int, step_size, "step_size for overlap_and_add"
      )
      if x.ndim < 2:
          raise ValueError(
              "Input must have (..., frames, frame_length) shape."
          )
  
      *batch_shape, nframes, segment_len = x.shape
      flat_batchsize = math.prod(batch_shape)
      x = x.reshape((flat_batchsize, nframes, segment_len))
      output_size = step_size * (nframes - 1) + segment_len
      nstep_per_segment = 1 + (segment_len - 1) // step_size
  
      # Here, we use shorter notation for axes.
      # B: batch_size, N: nframes, S: nstep_per_segment,
      # T: segment_len divided by S
  
      padded_segment_len = nstep_per_segment * step_size
      x = jnp.pad(
          x, ((0, 0), (0, 0), (0, padded_segment_len - segment_len))
      )
      x = x.reshape(
          (flat_batchsize, nframes, nstep_per_segment, step_size)
      )
  
      # For obtaining shifted signals, this routine reinterprets flattened array
      # with a shrinked axis.  With appropriate truncation/ padding, this operation
      # pushes the last padded elements of the previous row to the head of the
      # current row.
      # See implementation of `overlap_and_add` in Tensorflow for details.
      x = x.transpose((0, 2, 1, 3))  # x: (B, S, N, T)
      x = jnp.pad(
          x, ((0, 0), (0, 0), (0, nframes), (0, 0))
      )  # x: (B, S, N*2, T)
      shrinked = x.shape[2] - 1
      x = x.reshape((flat_batchsize, -1))
      x = x[:, : (nstep_per_segment * shrinked * step_size)]
      x = x.reshape(
          (flat_batchsize, nstep_per_segment, shrinked * step_size)
      )
  
      # Finally, sum shifted segments, and truncate results to the output_size.
      x = x.sum(axis=1)[:, :output_size]
      return x.reshape(tuple(batch_shape) + (-1,))
  
imports: from __future__ import annotations
  from collections.abc import Sequence
  from functools import partial
  import math
  import operator
  from typing import Callable
  import warnings
  import numpy
  import scipy.signal
  from scipy.fft import next_fast_len
  import jax
  import jax.numpy.fft
  import jax.numpy
  from jax import lax
  from jax._src.api_util import _ensure_index_tuple
  from jax._src import dtypes
  from jax._src.lax.lax import PrecisionLike
  from jax._src.numpy import linalg
  from jax._src.numpy.util import check_arraylike, _wraps, promote_dtypes_inexact, promote_dtypes_complex
  from jax._src.third_party.scipy import signal_helper
  from jax._src.typing import Array, ArrayLike
  from jax._src.util import canonicalize_axis, tuple_delete, tuple_insert
label_desc: Use image node_id 15 for calling a global function with example usage: _overlap_and_add(x, step_size) and returns: x