message: Update ConvLSTMCell to accept shape as list or tuple
diff: @@ -2071,7 +2071,7 @@ class ConvLSTMCell(rnn_cell_impl.RNNCell):
  conv_ndims: Convolution dimensionality (1, 2 or 3).
  input_shape: Shape of the input as int tuple, excluding the batch size.
  output_channels: int, number of output channels of the conv LSTM.
  - kernel_shape: Shape of kernel as in tuple (of size 1,2 or 3).
  + kernel_shape: Shape of kernel as an int tuple (of size 1, 2 or 3).
  use_bias: (bool) Use bias in convolutions.
  skip_connection: If set to `True`, concatenate the input to the
  output of the conv LSTM. Default: `False`.
  @@ -2092,7 +2092,7 @@ class ConvLSTMCell(rnn_cell_impl.RNNCell):
  self._conv_ndims = conv_ndims
  self._input_shape = input_shape
  self._output_channels = output_channels
  - self._kernel_shape = kernel_shape
  + self._kernel_shape = list(kernel_shape)
  self._use_bias = use_bias
  self._forget_bias = forget_bias
  self._skip_connection = skip_connection
  @@ -2172,7 +2172,7 @@ def _conv(args, filter_size, num_features, bias, bias_start=0.0):
  Args:
  args: a Tensor or a list of Tensors of dimension 3D, 4D or 5D,
  batch x n, Tensors.
  - filter_size: int tuple of filter height and width.
  + filter_size: int tuple of filter shape (of size 1, 2 or 3).
  num_features: int, number of features.
  bias: Whether to use biases in the convolution layer.
  bias_start: starting value to initialize the bias; 0 by default.
  