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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_variable(mesh, name, shape, dtype=tf.float32, master_dtype=None, slice_dtype=None, activation_dtype=None, initializer=None, trainable=True, **kwargs):
 """Create a new variable or retrieve an already-created one. Args: mesh: a Mesh name: a string (uses the existing tf.variable_scope()) shape: a Shape dtype: a VariableDType or a tf.DType master_dtype: an optional tf.DType (deprecated - use dtype arg) slice_dtype: an optional tf.DType (deprecated - use dtype arg) activation_dtype: an optional tf.DType (deprecated - use dtype arg) initializer: an optional tf initializer function trainable: a boolean **kwargs: additional keyword arguments to tf.get_variable Returns: a Tensor with the given shape and dtype equal to dtype.activation_dtype """

  if dtype is None:
    dtype = VariableDType(master_dtype, slice_dtype, activation_dtype)
  elif isinstance(dtype, tf.DType):
    dtype = VariableDType(
        master_dtype or dtype, slice_dtype or dtype, activation_dtype or dtype)
  elif not isinstance(dtype, VariableDType):
    raise ValueError("dtype should be a tf.dtype or a mtf.VariableDType")
  scope_name = tf.get_variable_scope().name
  if scope_name:
    full_name = scope_name + "/" + name
  else:
    full_name = name
  if full_name in mesh.graph.name_to_variable:
    var = mesh.graph.name_to_variable[full_name]
  else:
    var = Variable(
        mesh, name, convert_to_shape(shape), dtype, initializer, trainable,
        **kwargs)
    if var.name != full_name:
      raise ValueError(
          "Expected var.name == full_name.  %s vs %s" % (var.name, full_name))
    mesh.graph.name_to_variable[full_name] = var
  return var.outputs[0]