def logspace(start, stop, num=50, endpoint=True, base=10, dtype=None, axis=0):
    """Returns numbers spaced evenly on a log scale.

    In linear space, the sequence starts at `base ** start` and ends with
    `base ** stop` (see `endpoint` below).

    Args:
        start: The starting value of the sequence.
        stop: The final value of the sequence, unless `endpoint` is `False`.
            In that case, `num + 1` values are spaced over the interval in
            log-space, of which all but the last (a sequence of length `num`)
            are returned.
        num: Number of samples to generate. Defaults to `50`.
        endpoint: If `True`, `stop` is the last sample. Otherwise, it is not
            included. Defaults to `True`.
        base: The base of the log space. Defaults to `10`.
        dtype: The type of the output tensor.
        axis: The axis in the result to store the samples. Relevant only
            if start or stop are array-like.

    Note:
        Torch backend does not support `axis` argument.

    Returns:
        A tensor of evenly spaced samples on a log scale.
    """
    if any_symbolic_tensors((start, stop)):
        return Logspace(num, endpoint, base, dtype, axis)(start, stop)
    return backend.numpy.logspace(
        start,
        stop,
        num=num,
        endpoint=endpoint,
        base=base,
        dtype=dtype,
        axis=axis,
    )
Returns numbers spaced evenly on a log scale.

    In linear space, the sequence starts at `base ** start` and ends with
    `base ** stop` (see `endpoint` below).

    Args:
        start: The starting value of the sequence.
        stop: The final value of the sequence, unless `endpoint` is `False`.
            In that case, `num + 1` values are spaced over the interval in
            log-space, of which all but the last (a sequence of length `num`)
            are returned.
        num: Number of samples to generate. Defaults to `50`.
        endpoint: If `True`, `stop` is the last sample. Otherwise, it is not
            included. Defaults to `True`.
        base: The base of the log space. Defaults to `10`.
        dtype: The type of the output tensor.
        axis: The axis in the result to store the samples. Relevant only
            if start or stop are array-like.

    Note:
        Torch backend does not support `axis` argument.

    Returns:
        A tensor of evenly spaced samples on a log scale.
    
keras/src/ops/numpy.py