(value: Tensor or Number or tuple or list or Any)
Converts `value` to a `numpy.ndarray` where value must be a `Tensor`, backend tensor or tensor-like.
    If `value` is a `phi.math.Tensor`, this is equal to calling `phi.math.Tensor.numpy()`.

    *Note*: Using this function breaks the autograd chain. The returned tensor is not differentiable.
    To get a differentiable tensor, use `Tensor.native()` instead.

    Transposes the underlying tensor to match the name order and adds singleton dimensions for new dimension names.
    If a dimension of the tensor is not listed in `order`, a `ValueError` is raised.

    If `value` is a NumPy array, it may be returned directly.

    Returns:
        NumPy representation of `value`

    Raises:
        ValueError if the tensor cannot be transposed to match target_shape
Converts `value` to a `numpy.ndarray` where value must be a `Tensor`, backend tensor or tensor-like.
    If `value` is a `phi.math.Tensor`, this is equal to calling `phi.math.Tensor.numpy()`.
Converts value to a numpy . ndarray where value must be a Tensor backend tensor or tensor - like . If value is a phi . math . Tensor this is equal to calling phi . math . Tensor . numpy () .
def numpy(value: Tensor or Number or tuple or list or Any):
    """
    Converts `value` to a `numpy.ndarray` where value must be a `Tensor`, backend tensor or tensor-like.
    If `value` is a `phi.math.Tensor`, this is equal to calling `phi.math.Tensor.numpy()`.

    *Note*: Using this function breaks the autograd chain. The returned tensor is not differentiable.
    To get a differentiable tensor, use `Tensor.native()` instead.

    Transposes the underlying tensor to match the name order and adds singleton dimensions for new dimension names.
    If a dimension of the tensor is not listed in `order`, a `ValueError` is raised.

    If `value` is a NumPy array, it may be returned directly.

    Returns:
        NumPy representation of `value`

    Raises:
        ValueError if the tensor cannot be transposed to match target_shape
    """
    if isinstance(value, Tensor):
        return value.numpy()
    else:
        backend = choose_backend(value)
        return backend.numpy(value)
def numpy ( value : Tensor or Number or tuple or list or Any ) : if isinstance ( value , Tensor ) : return value . numpy ( ) else : backend = choose_backend ( value ) return backend . numpy ( value )