the number of tensor dimensions and the second dimension is
        the number of non-zero values.
  * **values** (*array_like*) -- Initial values for the tensor.
    Can be a list, tuple, NumPy "ndarray", scalar, and other
    types.

  * **size** (list, tuple, or "torch.Size", optional) -- Size of
    the sparse tensor. If not provided the size will be inferred
    as the minimum size big enough to hold all non-zero elements.

Keyword Arguments:
      * dtype ("torch.dtype", optional) -- the desired data type
        of returned tensor. Default: if None, infers data type from
        "values".
  * **device** ("torch.device", optional) -- the desired device of
    returned tensor. Default: if None, uses the current device for
    the default tensor type (see
    "torch.set_default_tensor_type()"). "device" will be the CPU
    for CPU tensor types and the current CUDA device for CUDA
    tensor types.
