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 initialize_hidden(
      self, batch_size: int, is_quantized: bool = False
  ) -> Tuple[Tensor, Tensor]:
      h, c = torch.zeros((batch_size, self.hidden_size)), torch.zeros(
          (batch_size, self.hidden_size)
      )
      if is_quantized:
          (h_scale, h_zp) = self.initial_hidden_state_qparams
          (c_scale, c_zp) = self.initial_cell_state_qparams
          h = torch.quantize_per_tensor(
              h,
              scale=h_scale,
              zero_point=h_zp,
              dtype=self.hidden_state_dtype,
          )
          c = torch.quantize_per_tensor(
              c,
              scale=c_scale,
              zero_point=c_zp,
              dtype=self.cell_state_dtype,
          )
      return h, c
  
imports: import numbers
  from typing import Optional, Tuple
  import warnings
  import torch
  from torch import Tensor
label_desc: Use image node_id 3 for calling the LSTMCell obj's underlying member method code with example usage: obj.initialize_hidden(batch_size, is_quantized) and returns: h,  c