code: def _update_slice(operand, update, start_indices, update_dims):
      """
      Similar to lax.dynamic_update_slice, but handles padded updates where padding
      values should not overwrite existing values in the array.
  
      Args:
      operand: the array to update
      update: the padded array to write
      start_indices: the offset at which to write `update`.
      update_dims: the true dimensions of the padded update `update`. Only values
        inside the rectangle given by `update_dims` will be overwritten.
      """
      operand_shape = operand.shape
      operand = lax.pad(
          operand,
          jnp.array(0, operand.dtype),
          [(0, d, 0) for d in update.shape],
      )
      start_indices = tuple(jnp.int32(i) for i in start_indices)
      t = lax.dynamic_slice(operand, start_indices, update.shape)
      t = _mask(update, update_dims, t)
      operand = lax.dynamic_update_slice(operand, t, start_indices)
      return lax.slice(operand, [0] * operand.ndim, operand_shape)
  
imports: from __future__ import annotations
  from functools import partial
  from typing import NamedTuple
  import jax
  import jax._src.numpy.lax_numpy
  import jax._src.numpy.linalg
  from jax._src.numpy import reductions
  from jax._src.numpy import ufuncs
  from jax import lax
  from jax._src.lax import qdwh
  from jax._src.lax import linalg
  from jax._src.lax.stack import Stack
label_desc: Use image node_id 4 for calling a global function with example usage: _update_slice(operand, update, start_indices, update_dims) and returns: lax