class_docstr: Cell class for the LSTM layer.
  
  This class processes one step within the whole time sequence input, whereas
  `keras.layer.LSTM` processes the whole sequence.
  
  Args:
      units: Positive integer, dimensionality of the output space.
      activation: Activation function to use. Default: hyperbolic tangent
          (`tanh`). If you pass None, no activation is applied
          (ie. "linear" activation: `a(x) = x`).
      recurrent_activation: Activation function to use for the recurrent step.
          Default: sigmoid (`sigmoid`). If you pass `None`, no activation is
          applied (ie. "linear" activation: `a(x) = x`).
      use_bias: Boolean, (default `True`), whether the layer
          should use a bias vector.
      kernel_initializer: Initializer for the `kernel` weights matrix,
          used for the linear transformation of the inputs. Default:
          `"glorot_uniform"`.
      recurrent_initializer: Initializer for the `recurrent_kernel`
          weights matrix, used for the linear transformation
          of the recurrent state. Default: `"orthogonal"`.
      bias_initializer: Initializer for the bias vector. Default: `"zeros"`.
      unit_forget_bias: Boolean (default `True`). If `True`,
          add 1 to the bias of the forget gate at initialization.
          Setting it to `True` will also force `bias_initializer="zeros"`.
          This is recommended in [Jozefowicz et al.](
          https://github.com/mlresearch/v37/blob/gh-pages/jozefowicz15.pdf)
      kernel_regularizer: Regularizer function applied to the `kernel` weights
          matrix. Default: `None`.
      recurrent_regularizer: Regularizer function applied to the
          `recurrent_kernel` weights matrix. Default: `None`.
      bias_regularizer: Regularizer function applied to the bias vector.
          Default: `None`.
      kernel_constraint: Constraint function applied to the `kernel` weights
          matrix. Default: `None`.
      recurrent_constraint: Constraint function applied to the
          `recurrent_kernel` weights matrix. Default: `None`.
      bias_constraint: Constraint function applied to the bias vector.
          Default: `None`.
      dropout: Float between 0 and 1. Fraction of the units to drop for the
          linear transformation of the inputs. Default: 0.
      recurrent_dropout: Float between 0 and 1. Fraction of the units to drop
          for the linear transformation of the recurrent state. Default: 0.
      seed: Random seed for dropout.
  
  Call arguments:
      inputs: A 2D tensor, with shape `(batch, features)`.
      states: A 2D tensor with shape `(batch, units)`, which is the state
          from the previous time step.
      training: Python boolean indicating whether the layer should behave in
          training mode or in inference mode. Only relevant when `dropout` or
          `recurrent_dropout` is used.
  
  Example:
  
  >>> inputs = np.random.random((32, 10, 8))
  >>> rnn = keras.layers.RNN(keras.layers.LSTMCell(4))
  >>> output = rnn(inputs)
  >>> output.shape
  (32, 4)
  >>> rnn = keras.layers.RNN(
  ...    keras.layers.LSTMCell(4),
  ...    return_sequences=True,
  ...    return_state=True)
  >>> whole_sequence_output, final_state = rnn(inputs)
  >>> whole_sequence_output.shape
  (32, 10, 4)
  >>> final_state.shape
  (32, 4)
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code: def get_config(self):
      config = {
          "units": self.units,
          "activation": activations.serialize(self.activation),
          "recurrent_activation": activations.serialize(
              self.recurrent_activation
          ),
          "use_bias": self.use_bias,
          "unit_forget_bias": self.unit_forget_bias,
          "kernel_initializer": initializers.serialize(
              self.kernel_initializer
          ),
          "recurrent_initializer": initializers.serialize(
              self.recurrent_initializer
          ),
          "bias_initializer": initializers.serialize(
              self.bias_initializer
          ),
          "kernel_regularizer": regularizers.serialize(
              self.kernel_regularizer
          ),
          "recurrent_regularizer": regularizers.serialize(
              self.recurrent_regularizer
          ),
          "bias_regularizer": regularizers.serialize(
              self.bias_regularizer
          ),
          "kernel_constraint": constraints.serialize(
              self.kernel_constraint
          ),
          "recurrent_constraint": constraints.serialize(
              self.recurrent_constraint
          ),
          "bias_constraint": constraints.serialize(
              self.bias_constraint
          ),
          "dropout": self.dropout,
          "recurrent_dropout": self.recurrent_dropout,
          "seed": self.seed,
      }
      base_config = super().get_config()
      return {**base_config, **config}
  
imports: import tree
  from keras import activations
  from keras import backend
  from keras import constraints
  from keras import initializers
  from keras import ops
  from keras import regularizers
  from keras.api_export import keras_export
  from keras.layers.input_spec import InputSpec
  from keras.layers.layer import Layer
  from keras.layers.rnn.dropout_rnn_cell import DropoutRNNCell
  from keras.layers.rnn.rnn import RNN
label_desc: Use image node_id 6 for calling the LSTMCell obj's underlying member method code with example usage: obj.get_config() and returns: dict