code: def _overlap_sequences(x, sequence_stride):
      # Ref: https://github.com/google/jax/blob/main/jax/_src/scipy/signal.py
      x = convert_to_tensor(x)
      *batch_shape, num_sequences, sequence_length = x.shape
      if sequence_stride > sequence_length:
          raise ValueError(
              "`sequence_stride` must equal or less than x.shape[-1]. "
              f"Received: sequence_stride={sequence_stride}, "
              f"x.shape[-1]={sequence_length}"
          )
      if sequence_stride < (sequence_length / num_sequences):
          raise ValueError(
              "`sequence_stride` must equal or greater than "
              "x.shape[-1] / x.shape[-2]. "
              f"Received: sequence_stride={sequence_stride}, "
              f"x.shape[-1]={sequence_length}, x.shape[-2]={num_sequences}"
          )
      flat_batchsize = math.prod(batch_shape)
      x = torch.reshape(
          x, (flat_batchsize, num_sequences, sequence_length)
      )
      output_size = (
          sequence_stride * (num_sequences - 1) + sequence_length
      )
      nstep_per_segment = 1 + (sequence_length - 1) // sequence_stride
      # Here, we use shorter notation for axes.
      # B: batch_size, N: num_sequences, S: nstep_per_segment,
      # T: sequence_length divided by S
      padded_segment_len = nstep_per_segment * sequence_stride
      x = torch.nn.functional.pad(
          x, (0, padded_segment_len - sequence_length, 0, 0, 0, 0)
      )
      x = torch.reshape(
          x,
          (
              flat_batchsize,
              num_sequences,
              nstep_per_segment,
              sequence_stride,
          ),
      )
      # 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 = torch.permute(x, (0, 2, 1, 3))  # x: (B, S, N, T)
      x = torch.nn.functional.pad(
          x, (0, 0, 0, num_sequences, 0, 0, 0, 0)
      )
      # x: (B, S, N*2, T)
      shrinked = x.shape[2] - 1
      x = torch.reshape(x, (flat_batchsize, -1))
      x = x[:, : (nstep_per_segment * shrinked * sequence_stride)]
      x = torch.reshape(
          x,
          (
              flat_batchsize,
              nstep_per_segment,
              shrinked * sequence_stride,
          ),
      )
      # Finally, sum shifted segments, and truncate results to the output_size.
      x = torch.sum(x, dim=1)[:, :output_size]
      return torch.reshape(x, tuple(batch_shape) + (-1,))
  
imports: import math
  import torch
  from keras.backend import standardize_dtype
  from keras.backend.torch.core import convert_to_tensor
  from keras.backend.torch.core import get_device
  from keras.backend.torch.numpy import pad
label_desc: Use image node_id 8 for calling a global function with example usage: _overlap_sequences(x, sequence_stride) and returns: torch