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

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

Start with ntm_cell.py and inspect the __call__ docstring alongside the cited input and output shape descriptions. Confirm the accepted input shape from the implementation, then update the documentation so the first dimension is described consistently; done when the docstring matches the behavior.

Written by the indexing model from the issue text.