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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 _build_ds_from_instruction(instruction, ds_from_file_fn):
 """Map an instruction to a real datasets for one particular shard. Args: instruction: A `dict` of `tf.Tensor` containing the instruction to load ds_from_file_fn: `fct`, function which returns the dataset associated to the filename Returns: dataset: `tf.data.Dataset`, The shard loaded from the instruction """

  # Create the example and mask ds for this particular shard
  examples_ds = ds_from_file_fn(instruction["filepath"])
  mask_ds = _build_mask_ds(
      mask_offset=instruction["mask_offset"],
      mask=instruction["mask"],
  )

  # Zip the mask and real examples
  ds = tf.data.Dataset.zip((examples_ds, mask_ds))
  # Filter according to the mask (only keep True)
  ds = ds.filter(lambda example, mask: mask)
  # Only keep the examples
  ds = ds.map(lambda example, mask: example)
  return ds