Cell's call: shouldn't the input's first dimension be of `batch` size?

[guillaume-chevalier]

I see for the description of the input tensor in the call function: inputs: input Tensor, 2D, 1 x input_size.

Shouldn't it rather be inputs: input Tensor, 2D, batch x input_size.?

It returns something 2D of the batch size. The training is so fast on my laptop compared to a normal LSTM that I am starting to doubt whether or not if it processes the full batch I am feeding to the cell. I assume that it accepts an input of shape batch x output_dim because the output of the call contains 2D tensors of batch size.

def __call__(self, input_, state=None, scope=None):
    """Run one step of NTM.
    Args:
        inputs: input Tensor, 2D, 1 x input_size.
        state: state Dictionary which contains M, read_w, write_w, read,
            output, hidden.
        scope: VariableScope for the created subgraph; defaults to class name.
    Returns:
        A tuple containing:
        - A 2D, batch x output_dim, Tensor representing the output of the LSTM
            after reading "input_" when previous state was "state".
            Here output_dim is:
                 num_proj if num_proj was set,
                 num_units otherwise.
        - A 2D, batch x state_size, Tensor representing the new state of LSTM
            after reading "input_" when previous state was "state".
    """

Found in:
https://github.com/carpedm20/NTM-tensorflow/blob/master/ntm_cell.py