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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 mask_from_embedding(emb):
 """Input embeddings -> padding mask. We have hacked symbol_modality to return all-zero embeddings for padding. Returns a mask with 0.0 in the padding positions and 1.0 elsewhere. Args: emb: a Tensor with shape [batch, width, height, depth]. Returns: a 0.0/1.0 Tensor with shape [batch, width, height, 1]. """

  return weights_nonzero(tf.reduce_sum(tf.abs(emb), axis=3, keepdims=True))