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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 flatten(inputs, scope=None):
 """Flattens the input while maintaining the batch_size. Assumes that the first dimension represents the batch. Args: scope: Optional scope for name_scope. Returns: a flattened tensor with shape [batch_size, k]. Raises: ValueError: if inputs.shape is wrong. """

  if len(inputs.get_shape()) < 2:
    raise ValueError('Inputs must be have a least 2 dimensions')
  dims = inputs.get_shape()[1:]
  k = dims.num_elements()
  with tf.name_scope(scope, 'Flatten', [inputs]):
    return tf.reshape(inputs, [-1, k])