class_docstr: Maintains a map from torch operations to their MIL values
  while building the graph. Can be used to process subgraphs recursively
  by pushing new context when stepping into a subgraph and popping that
  context when stepping out.
docstr: Arguments:
      ssa_var: Variable to add to the graph being constructed.
      torch_name: Optional unique string identifier of the operation. If
          omitted, it will use @ssa_var.name.
docstr_tok: ["Arguments",":","ssa_var",":","Variable","to","add","to","the","graph","being","constructed",".","torch_name",":","Optional","unique","string","identifier","of","the","operation",".","If","omitted",",","it","will","use","@","ssa_var.name","."]
code: def add(
      self,
      ssa_var: Var,
      torch_name: Optional[str] = None,
      override=False,
  ) -> None:
      """
      Arguments:
          ssa_var: Variable to add to the graph being constructed.
          torch_name: Optional unique string identifier of the operation. If
              omitted, it will use @ssa_var.name.
      """
      if torch_name is None:
          torch_name = ssa_var.name
      if torch_name in self._current_graph[-1] and not override:
          logger.warning(f"Torch var {torch_name} is added again.")
          return
      self._current_graph[-1][torch_name] = ssa_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 7 for calling the TranscriptionContext obj's underlying member method code with example usage: obj.add(ssa_var, torch_name, override) without return types