code: def _torch_upsample_to_core_upsample_block(block):
      for op in block.operations[:]:
          for b in op.blocks:
              _torch_upsample_to_core_upsample_block(b)
  
          if op.op_type in target_ops:
              if _try_replace_with_core_upsample(op):
                  logger.info(
                      "Successfully map {} to core upsample".format(
                          op.op_type
                      )
                  )
              else:
                  raise ValueError(
                      "Unable to map {} to core upsample".format(
                          op.op_type
                      )
                  )
  
file_path: coremltools/coremltools/converters/mil/frontend/torch/ssa_passes/torch_upsample_to_core_upsample.py
imports: import numpy
  from coremltools import _logger
  from coremltools.converters.mil.mil import Builder
  from coremltools.converters.mil.mil.passes.graph_pass import AbstractGraphPass
  from coremltools.converters.mil.mil.passes.helper import block_context_manager
  from coremltools.converters.mil.mil.passes.pass_registry import register_pass
label_desc: Use image node_id 1 for calling a global function with example usage: _torch_upsample_to_core_upsample_block(block) without return types