parameters: (predictions, targets, k)
return_statement: return nn.in_top_k(predictions, targets, k)
docstring: Returns whether the `targets` are in the top `k` `predictions`.
  
    Args:
        predictions: A tensor of shape `(batch_size, classes)` and type `float32`.
        targets: A 1D tensor of length `batch_size` and type `int32` or `int64`.
        k: An `int`, number of top elements to consider.
  
    Returns:
        A 1D tensor of length `batch_size` and type `bool`.
        `output[i]` is `True` if `predictions[i, targets[i]]` is within top-`k`
        values of `predictions[i]`.
docstring_summary: Returns whether the `targets` are in the top `k` `predictions`.
docstring_tokens: Returns whether the targets are in the top k predictions .
function: def in_top_k(predictions, targets, k):
    """Returns whether the `targets` are in the top `k` `predictions`.
  
    Args:
        predictions: A tensor of shape `(batch_size, classes)` and type `float32`.
        targets: A 1D tensor of length `batch_size` and type `int32` or `int64`.
        k: An `int`, number of top elements to consider.
  
    Returns:
        A 1D tensor of length `batch_size` and type `bool`.
        `output[i]` is `True` if `predictions[i, targets[i]]` is within top-`k`
        values of `predictions[i]`.
    """
    return nn.in_top_k(predictions, targets, k)
function_tokens: def in_top_k ( predictions , targets , k ) : return nn . in_top_k ( predictions , targets , k )