Describe the bug

The documentation of the class BinaryDiscreteTensorSpec can be confusing, as pointed out in #2344 (reply in thread).

Here is the documentation: https://github.com/pytorch/rl/blob/main/torchrl/data/tensor_specs.py#L3228
Which says,

"""A binary discrete tensor spec.

Args:
    n (int): length of the binary vector.
    shape (torch.Size, optional): total shape of the sampled tensors.
        If provided, the last dimension must match n.
    device (str, int or torch.device, optional): device of the tensors.
    dtype (str or torch.dtype, optional): dtype of the tensors. Defaults to torch.long.

Examples:
    >>> spec = BinaryDiscreteTensorSpec(n=4, shape=(5, 4), device="cpu", dtype=torch.bool)
    >>> print(spec.zero())
"""

The n argument

At the moment, since BinaryDiscreteTensorSpec inherits from DiscreteTensorSpec, n controls the number of outputs. n=1 means only False values, n=2 allows True and False values and n>2 also allows True and False . This is not very intuitive given the explanation in the documentation. Also, is this the desired behaviour?

Additionally, n has to match the last dimension of the shape and an error is raised otherwise. Is this necessary?

Possible simplification

We could remove the n parameter (fixing it to 2) and simplify the init method signature by allowing the user to define the spec shape only with the parameter shape.

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import torchrl

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import torchrl, numpy, sys
print(torchrl.__version__, numpy.__version__, sys.version, sys.platform)

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