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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def softmax_cross_entropy_one_hot(logits, labels, weights_fn=None):
 """Calculate softmax cross entropy given one-hot labels and logits. Args: logits: Tensor of size [batch-size, o=1, p=1, num-classes] labels: Tensor of size [batch-size, o=1, p=1, num-classes] weights_fn: Function that takes in labels and weighs examples (unused) Returns: cross-entropy (scalar), weights """

  with tf.variable_scope("softmax_cross_entropy_one_hot",
                         values=[logits, labels]):
    del weights_fn
    cross_entropy = tf.losses.softmax_cross_entropy(
        onehot_labels=labels, logits=logits)
    return cross_entropy, tf.constant(1.0)