code: def lstm(
      input: torch.Tensor,
      initial_states: Tuple[torch.Tensor],
      all_weights: Tuple[torch.Tensor],
      has_biases: bool,
      num_layers: int,
      dropout: float,
      train: bool,
      bidirectional: bool,
      batch_first: bool = False,
      batch_sizes: Sequence = None,
  ):
      return torch.lstm(
          input,
          initial_states,
          all_weights,
          has_biases,
          num_layers,
          dropout,
          train,
          bidirectional,
          batch_first,
      )
  
imports: from typing import Optional, Tuple, Union, Sequence
  import torch
  import ivy
  from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
  from .None import backend_version
  from ivy.functional.ivy.layers import _get_embed_dim, _handle_padding, _deconv_length
label_desc: Use image node_id 21 for calling a global function with example usage: lstm(input, initial_states, all_weights, has_biases, num_layers, dropout, train, bidirectional, batch_first, batch_sizes) and returns: torch