How to use the tensorly.backend.reshape function in tensorly

Notes
    -----
    This version works by first computing the mode-0 unfolding of the tensor
    and then refolding it.

    There are other possible and equivalent alternate implementation, e.g.
    summing over r and updating an outer product of vectors.

    """
    shape, rank = _validate_kruskal_tensor(kruskal_tensor)
    weights, factors = kruskal_tensor

    if weights is None:
        weights = T.ones(rank, **T.context(factors[0]))
    weights = T.reshape(weights, (1, rank))

    if mask is None:
        full_tensor = T.dot(factors[0]*weights,
                             T.transpose(khatri_rao(factors, skip_matrix=0)))
    else:
        full_tensor = T.sum(khatri_rao([factors[0]*weights]+factors[1:], mask=mask), axis=1)

    return fold(full_tensor, 0, shape)

def covariance(y_true, y_pred, axis=None):
    centered_true = T.mean(y_true, axis=axis)
    centered_pred = T.mean(y_pred, axis=axis)

    if axis is not None:
        # TODO: write a function to do this..
        shape = list(T.shape(y_true))
        shape[axis] = 1
        centered_true = T.reshape(centered_true, shape)
        shape = list(T.shape(y_pred))
        shape[axis] = 1
        centered_pred = T.reshape(centered_pred, shape)

    return T.mean((y_true - centered_true)*(y_pred - centered_pred), axis=axis)

def covariance(y_true, y_pred, axis=None):
    centered_true = T.mean(y_true, axis=axis)
    centered_pred = T.mean(y_pred, axis=axis)

    if axis is not None:
        # TODO: write a function to do this..
        shape = list(T.shape(y_true))
        shape[axis] = 1
        centered_true = T.reshape(centered_true, shape)
        shape = list(T.shape(y_pred))
        shape[axis] = 1
        centered_pred = T.reshape(centered_pred, shape)

    return T.mean((y_true - centered_true)*(y_pred - centered_pred), axis=axis)

-------
    ndarray
        partially unfolded tensor
    """
    if ravel_tensors:
        new_shape = [-1]
    else:
        new_shape = [tensor.shape[mode + skip_begin], -1]

    if skip_begin:
        new_shape = [tensor.shape[i] for i in range(skip_begin)] + new_shape

    if skip_end:
        new_shape += [tensor.shape[-i] for i in range(skip_end)]

    return T.reshape(T.moveaxis(tensor, mode+skip_begin, skip_begin), new_shape)

shape : tuple
        the shape of the original full tensor (including skipped dimensions)
    skip_begin : int, optional, default is 1
        number of dimensions to leave untouched at the beginning
    skip_end : int, optional
        number of dimensions to leave untouched at the end
    
    Returns
    -------
    ndarray
        partially re-folded tensor
    """
    transposed_shape = list(shape)
    mode_dim = transposed_shape.pop(skip_begin + mode)
    transposed_shape.insert(skip_begin, mode_dim)
    return T.moveaxis(T.reshape(unfolded, transposed_shape), skip_begin, skip_begin + mode)