class_docstr: This is a helper class that provides housekeeping for LSTM cells.
  
  This may be useful for alternative LSTM and similar type of cells.
  The subclasses must implement `_call_cell` method and `num_units` property.
docstr: Run this LSTM on inputs, starting from the given state.
  
  This method must be implemented by subclasses and does the actual work
  of calling the cell.
  
  Args:
    inputs: `3-D` tensor with shape `[time_len, batch_size, input_size]`
    initial_cell_state: initial value for cell state, shape `[batch_size,
      self._num_units]`
    initial_output: initial value of cell output, shape `[batch_size,
      self._num_units]`
    dtype: The data type for the initial state and expected output.
    sequence_length: Specifies the length of each sequence in inputs. An int32
      or int64 vector (tensor) size [batch_size], values in [0, time_len) or
        None.
  
  Returns:
    A pair containing:
  
    - State: A `3-D` tensor of shape `[time_len, batch_size, output_size]`
    - Output: A `3-D` tensor of shape `[time_len, batch_size, output_size]`
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code: def _call_cell(
      self,
      inputs,
      initial_cell_state,
      initial_output,
      dtype,
      sequence_length,
  ):
      """Run this LSTM on inputs, starting from the given state.
  
      This method must be implemented by subclasses and does the actual work
      of calling the cell.
  
      Args:
        inputs: `3-D` tensor with shape `[time_len, batch_size, input_size]`
        initial_cell_state: initial value for cell state, shape `[batch_size,
          self._num_units]`
        initial_output: initial value of cell output, shape `[batch_size,
          self._num_units]`
        dtype: The data type for the initial state and expected output.
        sequence_length: Specifies the length of each sequence in inputs. An int32
          or int64 vector (tensor) size [batch_size], values in [0, time_len) or
            None.
  
      Returns:
        A pair containing:
  
        - State: A `3-D` tensor of shape `[time_len, batch_size, output_size]`
        - Output: A `3-D` tensor of shape `[time_len, batch_size, output_size]`
      """
      pass
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import abc
  import six
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.keras.engine import input_spec
  from tensorflow.python.layers import base
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gen_rnn_ops
  from tensorflow.python.ops import init_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import nn_ops
  from tensorflow.python.ops import rnn_cell_impl
label_desc: Use image node_id 2 for calling the LSTMBlockWrapper obj's underlying member method code with example usage: obj._call_cell(inputs, initial_cell_state, initial_output, dtype, sequence_length) without return types