class_docstr: Utilities to manage information pertaining to quantization of tensors in a PyTorch graph.
docstr: Stores the quantization parameters (torch dtype, scale, zero_point) corresponding to a named
  var in the graph.
  zero_point should be in a NumPy dtype corresponding to torch one (for e.g. np.uint8 for torch.quint8).
docstr_tok: ["Stores","the","quantization","parameters","(","torch","dtype",",","scale",",","zero_point",")","corresponding","to","a","named","var","in","the","graph",".","zero_point","should","be","in","a","NumPy","dtype","corresponding","to","torch","one","(","for","e.g",".","np.uint8","for","torch.quint8",")","."]
code: def add_quantization_info(
      self, name, torch_dtype, scale, zero_point, axis=None
  ):
      """
      Stores the quantization parameters (torch dtype, scale, zero_point) corresponding to a named
      var in the graph.
      zero_point should be in a NumPy dtype corresponding to torch one (for e.g. np.uint8 for torch.quint8).
      """
      self._quant_param_map[name] = (
          torch_dtype,
          scale,
          zero_point,
          axis,
      )
  
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 2 for calling the QuantizationContext obj's underlying member method code with example usage: obj.add_quantization_info(name, torch_dtype, scale, zero_point, axis) without return types