class_docstr: Class that handles conversion of pytorch models to the MIL format.
  
  Models passed to the @TorchConverter go from:
  Loaded-Torch Model -> Internal Graph -> PyMIL
code: def _preprocess_input_vars(input_var):
      if (
          types.is_tensor(input_var.sym_type)
          or types.is_scalar(input_var.sym_type)
      ) and input_var.dtype == types.fp16:
          input_var = mb.cast(x=input_var, dtype="fp32")
      return input_var
  
imports: from collections import OrderedDict
  from typing import List, Optional, Union
  import numpy
  import torch
  from torch.jit._script import RecursiveScriptModule
  from coremltools import _logger
  from coremltools._deps import _HAS_TORCH_EXPORT_API
  from coremltools.converters.mil._deployment_compatibility import AvailableTarget
  from coremltools.converters.mil.input_types import ImageType, InputType, TensorType
  from coremltools.converters.mil.mil import Builder
  from coremltools.converters.mil.mil import Function, Placeholder, Program, types
  from coremltools.converters.mil.mil.types import is_float
  from coremltools.converters.mil.mil.var import Var
  from .._utils import get_output_names
  from .internal_graph import InternalTorchIRGraph, InternalTorchIRNode
  from .ops import TorchFrontend, convert_nodes
  from .quantization_ops import _dequantized_weight
  from .torch_op_registry import _TORCH_OPS_REGISTRY
  from .torchir_passes import flatten_graph_input_values, flatten_graph_output_values, generate_tensor_assignment_ops, remove_getattr_nodes, transform_inplace_ops
  from .torchscript_utils import torch_to_mil_types
label_desc: Use image node_id 6 for calling the TorchConverter obj's underlying member method code with example usage: obj._preprocess_input_vars(input_var) and returns: input_var