code: def compress_weights(
      weights: np.ndarray,
      weights_zp: int,
      weights_layout: str,
      ifm_bitdepth: int,
      block_depth: int,
      dilation: Tuple[int, int],
      accel_config: vapi.NpuAccelerator,
      is_depthwise: Optional[bool] = False,
  ) -> bytearray:
      """The NPU requires the weights to be compressed
      to be executed. Therefore, this function calls into
      the Vela APIs to compress the weights.
  
      Parameters
      ----------
      weights : numpy.ndarray
          The raw weights
      weights_zp : int
          The zero point of the weights
      weights_layout : str
          A string literal indicating the layout
          Supported values : HWIO, HWOI, OHWI
      ifm_bitdepth : int
          The bit depth of the ifm the weights are used with
      block_depth : int
          The depth of the optimal block config for the operator
      dilation : tuple
          A tuple of 2 elements indicating dilation in h and w
      accel_config : ethosu.vela.api.NpuAccelerator
          The NPU accelerator config
      is_depthwise : bool, Optional
          This indicates whether the weights are compressed for depthwise convolution
  
      Returns
      -------
      compressed_weights : bytearray
          Compressed weights
      """
      layout_transform_indices = {
          "HWIO": (3, 0, 1, 2),
          "HWOI": (2, 0, 1, 3),
          "OHWI": (0, 1, 2, 3),
      }
      assert weights_layout in layout_transform_indices.keys()
      assert isinstance(weights_zp, np.int64)
      weights = weights.astype(np.int16) - weights_zp
      # Vela needs the weights in OHWI layout
      weights_ohwi = np.transpose(
          weights, layout_transform_indices[weights_layout]
      )
      shape_ohwi = [
          weights.shape[layout_transform_indices[weights_layout][0]],
          weights.shape[layout_transform_indices[weights_layout][1]],
          weights.shape[layout_transform_indices[weights_layout][2]],
          weights.shape[layout_transform_indices[weights_layout][3]],
      ]
      block_traversal = calculate_block_traversal_mode(
          is_depthwise, shape_ohwi, ifm_bitdepth
      )
  
      compressed_weights = vapi.npu_encode_weights(
          accelerator=accel_config,
          weights_volume=weights_ohwi,
          dilation_xy=dilation,
          ifm_bitdepth=ifm_bitdepth,
          ofm_block_depth=block_depth,
          is_depthwise=is_depthwise,
          block_traversal=block_traversal,
      )
      return compressed_weights
  
imports: import logging
  import math
  from typing import List, Optional, Tuple
  import numpy
  from ethosu.vela import api
  from ethosu.vela.architecture_allocator import find_block_config
  from ethosu.vela.architecture_features import Accelerator, create_default_arch
  from ethosu.vela.operation import NpuBlockType
  from ethosu.vela.register_command_stream_generator import resampling_mode_map
  from ethosu.vela.register_command_stream_util import to_kernel
  from ethosu.vela.shape4d import Shape4D
  import tvm
  from tvm.relay.backend.contrib.ethosu import tir_to_cs_translator
  from tvm.relay.backend.contrib.ethosu import util
label_desc: Use image node_id 5 for calling a global function with example usage: compress_weights(weights, weights_zp, weights_layout, ifm_bitdepth, block_depth, dilation, accel_config, is_depthwise) and returns: compressed_weights