class_docstr: Try to map Torch dialect ops
  1. `torch_upsample_nearest_neighbor`
  2. `torch_upsample_bilinear`
  to `upsample_nearest_neighbor` or `upsample_bilinear` in the core op set if compatible.
  
  Inputs:
  
      prog: Program
code: def apply(self, prog):
      for f in prog.functions.values():
          _torch_upsample_to_core_upsample_block(f)
  
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 the torch_upsample_to_core_upsample obj's underlying member method code with example usage: obj.apply(prog) without return types