def to_torch_tensor(x):
    """Change to torch.Tensor or ComplexTensor from numpy.ndarray.

    Args:
        x: Inputs. It should be one of numpy.ndarray, Tensor, ComplexTensor, and dict.

    Returns:
        Tensor or ComplexTensor: Type converted inputs.

    Examples:
        >>> xs = np.ones(3, dtype=np.float32)
        >>> xs = to_torch_tensor(xs)
        tensor([1., 1., 1.])
        >>> xs = torch.ones(3, 4, 5)
        >>> assert to_torch_tensor(xs) is xs
        >>> xs = {'real': xs, 'imag': xs}
        >>> to_torch_tensor(xs)
        ComplexTensor(
        Real:
        tensor([1., 1., 1.])
        Imag;
        tensor([1., 1., 1.])
        )

    """
    # If numpy, change to torch tensor
    if isinstance(x, np.ndarray):
        if x.dtype.kind == "c":
            # Dynamically importing because torch_complex requires python3
            from torch_complex.tensor import ComplexTensor

            return ComplexTensor(x)
        else:
            return torch.from_numpy(x)

    # If {'real': ..., 'imag': ...}, convert to ComplexTensor
    elif isinstance(x, dict):
        # Dynamically importing because torch_complex requires python3
        from torch_complex.tensor import ComplexTensor

        if "real" not in x or "imag" not in x:
            raise ValueError("has 'real' and 'imag' keys: {}".format(list(x)))
        # Relative importing because of using python3 syntax
        return ComplexTensor(x["real"], x["imag"])

    # If torch.Tensor, as it is
    elif isinstance(x, torch.Tensor):
        return x

    else:
        error = (
            "x must be numpy.ndarray, torch.Tensor or a dict like "
            "{{'real': torch.Tensor, 'imag': torch.Tensor}}, "
            "but got {}".format(type(x))
        )
        try:
            from torch_complex.tensor import ComplexTensor
        except Exception:
            # If PY2
            raise ValueError(error)
        else:
            # If PY3
            if isinstance(x, ComplexTensor):
                return x
            else:
                raise ValueError(error)
Change to torch.Tensor or ComplexTensor from numpy.ndarray.

    Args:
        x: Inputs. It should be one of numpy.ndarray, Tensor, ComplexTensor, and dict.

    Returns:
        Tensor or ComplexTensor: Type converted inputs.

    Examples:
        >>> xs = np.ones(3, dtype=np.float32)
        >>> xs = to_torch_tensor(xs)
        tensor([1., 1., 1.])
        >>> xs = torch.ones(3, 4, 5)
        >>> assert to_torch_tensor(xs) is xs
        >>> xs = {'real': xs, 'imag': xs}
        >>> to_torch_tensor(xs)
        ComplexTensor(
        Real:
        tensor([1., 1., 1.])
        Imag;
        tensor([1., 1., 1.])
        )

    
train/target_speaker_extraction/models/av_tfgridnetV3/espnet2/nets_utils.py