parameters: (op)
return_statement: return [tensor_shape.vector(batch_size.value), input_shape]
docstring: Shape function for SoftmaxCrossEntropyWithLogits op.
docstring_summary: Shape function for SoftmaxCrossEntropyWithLogits op.
docstring_tokens: Shape function for SoftmaxCrossEntropyWithLogits op .
function: def _SoftmaxCrossEntropyWithLogitsShape(op):
    """Shape function for SoftmaxCrossEntropyWithLogits op."""
    logits_shape = op.inputs[0].get_shape()
    labels_shape = op.inputs[1].get_shape()
    input_shape = logits_shape.merge_with(labels_shape).with_rank(2)
    batch_size = input_shape[0]
    return [tensor_shape.vector(batch_size.value), input_shape]
function_tokens: def _SoftmaxCrossEntropyWithLogitsShape ( op ) : logits_shape = op . inputs [ 0 ] . get_shape ( ) labels_shape = op . inputs [ 1 ] . get_shape ( ) input_shape = logits_shape . merge_with ( labels_shape ) . with_rank ( 2 ) batch_size = input_shape [ 0 ] return [ tensor_shape . vector ( batch_size . value ) , input_shape ]