class_docstr: Wrapper class for quantized operations.
  
  The instance of this class can be used instead of the
  ``torch.ops.quantized`` prefix. See example usage below.
  
  .. note::
  
      This class does not provide a ``forward`` hook. Instead, you must use
      one of the underlying functions (e.g. ``add``).
  
  Examples::
  
      >>> q_add = QFunctional()
      >>> # xdoctest: +SKIP
      >>> a = torch.quantize_per_tensor(torch.tensor(3.0), 1.0, 0, torch.qint32)
      >>> b = torch.quantize_per_tensor(torch.tensor(4.0), 1.0, 0, torch.qint32)
      >>> q_add.add(a, b)  # Equivalent to ``torch.ops.quantized.add(a, b, 1.0, 0)``
  
  Valid operation names:
      - add
      - cat
      - mul
      - add_relu
      - add_scalar
      - mul_scalar
code: def from_float(cls, mod):
      assert (
          type(mod) == FloatFunctional
      ), "QFunctional.from_float expects an instance of FloatFunctional"
      scale, zero_point = mod.activation_post_process.calculate_qparams()  # type: ignore[operator]
      new_mod = QFunctional()
      new_mod.scale = float(scale)
      new_mod.zero_point = int(zero_point)
      return new_mod
  
imports: from typing import List
  import torch
  from torch import Tensor
  from torch._ops import ops
label_desc: Use image node_id 14 for calling the QFunctional obj's underlying member method code with example usage: obj.from_float(cls, mod) and returns: new_mod