code: def __call__(self, x, hc):
      @TinyJit
      def _do_step(x_, hc_):
          return self.do_step(x_, hc_)
  
      if hc is None:
          hc = Tensor.zeros(
              self.layers,
              2 * x.shape[1],
              self.hidden_size,
              requires_grad=False,
          )
  
      output = None
      for t in range(x.shape[0]):
          hc = _do_step(
              x[t] + 1 - 1, hc
          )  # TODO: why do we need to do this?
          if output is None:
              output = hc[-1:, : x.shape[1]]
          else:
              output = output.cat(
                  hc[-1:, : x.shape[1]], dim=0
              ).realize()
  
      return output, hc
  
imports: from tinygrad.tensor import Tensor
  from tinygrad.jit import TinyJit
  from tinygrad.nn import Linear, Embedding
  from tinygrad.helpers import fetch
  import numpy
  from pathlib import Path
label_desc: Use image node_id 2 for calling the LSTM obj's underlying member method code with example usage: obj.__call__(x, hc) and returns: output,  hc, self