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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def conv_lstm_2d(inputs, state, output_channels, kernel_size=5, name=None, spatial_dims=None):
 """2D Convolutional LSTM."""

  input_shape = common_layers.shape_list(inputs)
  batch_size, input_channels = input_shape[0], input_shape[-1]
  if spatial_dims is None:
    input_shape = input_shape[1:]
  else:
    input_shape = spatial_dims + [input_channels]

  cell = tf.contrib.rnn.ConvLSTMCell(
      2, input_shape, output_channels,
      [kernel_size, kernel_size], name=name)
  if state is None:
    state = cell.zero_state(batch_size, tf.float32)
  outputs, new_state = cell(inputs, state)
  return outputs, new_state