class_docstr: 2D Convolutional LSTM layer.
  
  A convolutional LSTM is similar to an LSTM, but the input transformations
  and recurrent transformations are both convolutional. This layer is typically
  used to process timeseries of images (i.e. video-like data).
  
  It is known to perform well for weather data forecasting,
  using inputs that are timeseries of 2D grids of sensor values.
  It isn't usually applied to regular video data, due to its high computational
  cost.
  
  Args:
    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, ...)`.
      It defaults to the `image_data_format` value found in your
      Keras config file at `~/.keras/keras.json`.
      If you never set it, then it will be "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.
    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.
    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.
  
  Call arguments:
    inputs: A 5D float tensor (see input shape description below).
    mask: Binary tensor of shape `(samples, timesteps)` indicating whether
      a given timestep should be masked.
    training: Python boolean indicating whether the layer should behave in
      training mode or in inference mode. This argument is passed to the cell
      when calling it. This is only relevant if `dropout` or `recurrent_dropout`
      are set.
    initial_state: List of initial state tensors to be passed to the first
      call of the cell.
  
  Input shape:
    - If data_format='channels_first'
        5D tensor with shape:
        `(samples, time, channels, rows, cols)`
    - If data_format='channels_last'
        5D tensor with shape:
        `(samples, time, rows, cols, channels)`
  
  Output shape:
    - If `return_state`: a list of tensors. The first tensor is
      the output. The remaining tensors are the last states,
      each 4D tensor with shape:
      `(samples, filters, new_rows, new_cols)`
      if data_format='channels_first'
      or 4D tensor with shape:
      `(samples, new_rows, new_cols, filters)`
      if data_format='channels_last'.
      `rows` and `cols` values might have changed due to padding.
    - If `return_sequences`: 5D tensor with shape:
      `(samples, timesteps, filters, new_rows, new_cols)`
      if data_format='channels_first'
      or 5D tensor with shape:
      `(samples, timesteps, new_rows, new_cols, filters)`
      if data_format='channels_last'.
    - Else, 4D tensor with shape:
      `(samples, filters, new_rows, new_cols)`
      if data_format='channels_first'
      or 4D tensor with shape:
      `(samples, new_rows, new_cols, filters)`
      if data_format='channels_last'.
  
  Raises:
    ValueError: in case of invalid constructor arguments.
  
  References:
    - [Shi et al., 2015](http://arxiv.org/abs/1506.04214v1)
    (the current implementation does not include the feedback loop on the
    cells output).
  
  Example:
  
  ```python
  steps = 10
  height = 32
  width = 32
  input_channels = 3
  output_channels = 6
  
  inputs = tf.keras.Input(shape=(steps, height, width, input_channels))
  layer = tf.keras.layers.ConvLSTM2D(filters=output_channels, kernel_size=3)
  outputs = layer(inputs)
  ```
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code: def activation(self):
      return self.cell.activation
  
imports: import numpy
  from tensorflow.python.keras import activations
  from tensorflow.python.keras import backend
  from tensorflow.python.keras import constraints
  from tensorflow.python.keras import initializers
  from tensorflow.python.keras import regularizers
  from tensorflow.python.keras.engine.base_layer import Layer
  from tensorflow.python.keras.engine.input_spec import InputSpec
  from tensorflow.python.keras.layers.recurrent import DropoutRNNCellMixin
  from tensorflow.python.keras.layers.recurrent import RNN
  from tensorflow.python.keras.utils import conv_utils
  from tensorflow.python.keras.utils import generic_utils
  from tensorflow.python.keras.utils import tf_utils
  from tensorflow.python.ops import array_ops
label_desc: Use image node_id 9 for calling the ConvLSTM2D obj's underlying member method code with example usage: obj.activation() and returns: self