class_docstr: A quantizable long short-term memory (LSTM) cell.
  
  For the description and the argument types, please, refer to :class:`~torch.nn.LSTMCell`
  
  Examples::
  
      >>> import torch.ao.nn.quantizable as nnqa
      >>> rnn = nnqa.LSTMCell(10, 20)
      >>> input = torch.randn(6, 10)
      >>> hx = torch.randn(3, 20)
      >>> cx = torch.randn(3, 20)
      >>> output = []
      >>> for i in range(6):
      ...     hx, cx = rnn(input[i], (hx, cx))
      ...     output.append(hx)
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code: def from_float(cls, other):
      assert type(other) == cls._FLOAT_MODULE
      assert hasattr(
          other, "qconfig"
      ), "The float module must have 'qconfig'"
      observed = cls.from_params(
          other.weight_ih, other.weight_hh, other.bias_ih, other.bias_hh
      )
      observed.qconfig = other.qconfig
      observed.igates.qconfig = other.qconfig
      observed.hgates.qconfig = other.qconfig
      return observed
  
imports: import numbers
  from typing import Optional, Tuple
  import warnings
  import torch
  from torch import Tensor
label_desc: Use image node_id 6 for calling the LSTMCell obj's underlying member method code with example usage: obj.from_float(cls, other) and returns: observed