class_docstr: Cell class for the ConvLSTM layer.
  
  Args:
      rank: Integer, rank of the convolution, e.g. "2" for 2D convolutions.
      filters: Integer, the dimensionality of the output space
          (i.e. the number of output filters in the convolution).
      kernel_size: An integer or tuple/list of n integers, specifying the
          dimensions of the convolution window.
      strides: An integer or tuple/list of n integers, specifying the strides
          of the convolution. Specifying any stride value != 1
          is incompatible with specifying any `dilation_rate` value != 1.
      padding: One of `"valid"` or `"same"` (case-insensitive).
          `"valid"` means no padding. `"same"` results in padding evenly
          to the left/right or up/down of the input such that output
          has the same height/width dimension as the input.
      data_format: A string, one of `channels_last` (default) or
          `channels_first`. When unspecified, uses
          `image_data_format` value found in your Keras config file at
          `~/.keras/keras.json` (if exists) else 'channels_last'.
          Defaults to `'channels_last'`.
      dilation_rate: An integer or tuple/list of n integers, specifying the
          dilation rate to use for dilated convolution.
          Currently, specifying any `dilation_rate` value != 1 is
          incompatible with specifying any `strides` value != 1.
      activation: Activation function. If `None`, no activation is applied.
      recurrent_activation: Activation function to use for the recurrent step.
      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`.
      activity_regularizer: Regularizer function applied to the output of the
          layer (its "activation"). 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 (2+ `rank`)D tensor.
      states:  List of state tensors corresponding to the previous timestep.
      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.
code: def get_config(self):
      config = {
          "filters": self.filters,
          "kernel_size": self.kernel_size,
          "strides": self.strides,
          "padding": self.padding,
          "data_format": self.data_format,
          "dilation_rate": self.dilation_rate,
          "activation": activations.serialize(self.activation),
          "recurrent_activation": activations.serialize(
              self.recurrent_activation
          ),
          "use_bias": self.use_bias,
          "kernel_initializer": initializers.serialize(
              self.kernel_initializer
          ),
          "recurrent_initializer": initializers.serialize(
              self.recurrent_initializer
          ),
          "bias_initializer": initializers.serialize(
              self.bias_initializer
          ),
          "unit_forget_bias": self.unit_forget_bias,
          "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.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
  from keras.ops import operation_utils
  from keras.utils import argument_validation
label_desc: Use image node_id 8 for calling the ConvLSTMCell obj's underlying member method code with example usage: obj.get_config() and returns: dict