code: def _set_symbolic_inputs(proto, symbolic_inputs):
      # Set symbolic input shapes by -1 inferred from graph
      for input_name, shape in symbolic_inputs.items():
          lb = [1 if is_symbolic(d) else d for d in shape]
          ub = [-1 if is_symbolic(d) else d for d in shape]
          set_multiarray_ndshape_range(
              proto, input_name, lower_bounds=lb, upper_bounds=ub
          )
  
file_path: coremltools/coremltools/converters/mil/backend/nn/load.py
imports: import coremltools
  from coremltools.converters._profile_utils import _profile
  from coremltools.converters.mil.backend.backend_helper import _get_probability_var_for_classifier
  from coremltools.converters.mil.input_types import ColorLayout, EnumeratedShapes, ImageType, RangeDim, Shape
  from coremltools.converters.mil.mil import types
  from coremltools.converters.mil.mil.types.symbolic import any_symbolic, any_variadic, is_symbolic
  from coremltools.models import MLModel
  from coremltools.models import neural_network
  from coremltools.models.datatypes import Array
  from coremltools.models.neural_network import flexible_shape_utils
  from coremltools.models.neural_network.flexible_shape_utils import add_enumerated_image_sizes, add_multiarray_ndshape_enumeration, set_multiarray_ndshape_range
  from ..backend_helper import _get_colorspace_enum, _validate_image_input_output_shapes
  from .op_mapping import convert_ops
label_desc: Use image node_id 4 for calling a global function with example usage: _set_symbolic_inputs(proto, symbolic_inputs) without return types