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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 get_tensor_num_entries(self, tensor_name, partial_layout=None, mesh_dimension_to_size=None):
 """The number of entries in a tensor. If partial_layout is specified, then mesh_dimension_to_size must also be. In this case, the number of entries on a single device is returned. Args: tensor_name: a string, name of a tensor in the graph. partial_layout: an optional {string: string}, from MTF dimension name to mesh dimension name. mesh_dimension_to_size: an optional {string: int}, from mesh dimension name to size. Returns: an integer """

    shape = self.get_tensor_shape(tensor_name)
    # We don't have to worry about divisiblity issues because Mesh TensorFlow
    # only allows evenly divisible assignments.
    num_entries = 1
    for dim in shape.dims:
      num_entries = num_entries * dim.value

    if not partial_layout:
      return num_entries

    for mtf_dimension_name in self.get_tensor_mtf_dimension_names(tensor_name):
      if mtf_dimension_name not in partial_layout:
        continue
      mesh_dimension_name = partial_layout[mtf_dimension_name]
      mesh_dimension_size = mesh_dimension_to_size[mesh_dimension_name]
      num_entries = int(math.ceil(num_entries / mesh_dimension_size))

    return num_entries