class_docstr: Abstract N-D Convolutional LSTM layer (used as implementation base).
  
  Similar to an LSTM layer, but the input transformations
  and recurrent transformations are both convolutional.
  
  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`.
          The ordering of the dimensions in the inputs.
          `channels_last` corresponds to inputs with shape
          `(batch, time, ..., channels)`
          while `channels_first` corresponds to
          inputs with shape `(batch, time, channels, ...)`.
          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 to use.
          By default hyperbolic tangent activation function is applied
          (`tanh(x)`).
      recurrent_activation: Activation function to use
          for the recurrent step.
      use_bias: Boolean, whether the layer uses a bias vector.
      kernel_initializer: Initializer for the `kernel` weights matrix,
          used for the linear transformation of the inputs.
      recurrent_initializer: Initializer for the `recurrent_kernel`
          weights matrix,
          used for the linear transformation of the recurrent state.
      bias_initializer: Initializer for the bias vector.
      unit_forget_bias: Boolean.
          If True, add 1 to the bias of the forget gate at initialization.
          Use in combination with `bias_initializer="zeros"`.
          This is recommended in [Jozefowicz et al., 2015](
          http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf)
      kernel_regularizer: Regularizer function applied to
          the `kernel` weights matrix.
      recurrent_regularizer: Regularizer function applied to
          the `recurrent_kernel` weights matrix.
      bias_regularizer: Regularizer function applied to the bias vector.
      activity_regularizer: Regularizer function applied to.
      kernel_constraint: Constraint function applied to
          the `kernel` weights matrix.
      recurrent_constraint: Constraint function applied to
          the `recurrent_kernel` weights matrix.
      bias_constraint: Constraint function applied to the bias vector.
      dropout: Float between 0 and 1.
          Fraction of the units to drop for
          the linear transformation of the inputs.
      recurrent_dropout: Float between 0 and 1.
          Fraction of the units to drop for
          the linear transformation of the recurrent state.
      seed: Random seed for dropout.
      return_sequences: Boolean. Whether to return the last output
          in the output sequence, or the full sequence. (default False)
      return_state: Boolean Whether to return the last state
          in addition to the output. (default False)
      go_backwards: Boolean (default False).
          If True, process the input sequence backwards.
      stateful: Boolean (default False). If True, the last state
          for each sample at index i in a batch will be used as initial
          state for the sample of index i in the following batch.
code: def call(
      self, sequences, initial_state=None, mask=None, training=False
  ):
      return super().call(
          sequences,
          initial_state=initial_state,
          mask=mask,
          training=training,
      )
  
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 2 for calling the ConvLSTM obj's underlying member method code with example usage: obj.call(sequences, initial_state, mask, training) and returns: super