(x, input_dims, n_steps)
return x_reformat
This function reformat input to the shape that standard RNN can take. 
    
    Inputs:
        x -- a tensor of shape (batch_size, n_steps, input_dims).
    Outputs:
        x_reformat -- a list of 'n_steps' tenosrs, each has shape (batch_size, input_dims).
This function reformat input to the shape that standard RNN can take. 
    
    Inputs:
        x -- a tensor of shape (batch_size, n_steps, input_dims).
    Outputs:
        x_reformat -- a list of 'n_steps' tenosrs, each has shape (batch_size, input_dims).
This function reformat input to the shape that standard RNN can take . Inputs : x -- a tensor of shape ( batch_size n_steps input_dims ) . Outputs : x_reformat -- a list of n_steps tenosrs each has shape ( batch_size input_dims ) .
def _rnn_reformat(x, input_dims, n_steps):
    """
    This function reformat input to the shape that standard RNN can take. 
    
    Inputs:
        x -- a tensor of shape (batch_size, n_steps, input_dims).
    Outputs:
        x_reformat -- a list of 'n_steps' tenosrs, each has shape (batch_size, input_dims).
    """
    # permute batch_size and n_steps
    x_ = tf.transpose(x, [1, 0, 2])
    # reshape to (n_steps*batch_size, input_dims)
    x_ = tf.reshape(x_, [-1, input_dims])
    # split to get a list of 'n_steps' tensors of shape (batch_size, input_dims)
    x_reformat = tf.split(x_, n_steps, 0)

    return x_reformat
def _rnn_reformat ( x , input_dims , n_steps ) : # permute batch_size and n_steps x_ = tf . transpose ( x , [ 1 , 0 , 2 ] ) # reshape to (n_steps*batch_size, input_dims) x_ = tf . reshape ( x_ , [ - 1 , input_dims ] ) # split to get a list of 'n_steps' tensors of shape (batch_size, input_dims) x_reformat = tf . split ( x_ , n_steps , 0 ) return x_reformat