code: def alphas(shape, alpha_value, name=None):
      
      with ops.name_scope(name, "alphas", [shape]) as name:
  
          alpha_tensor = convert_to_tensor(alpha_value)
          alpha_dtype = dtypes.as_dtype(alpha_tensor.dtype).base_dtype
  
          if not isinstance(shape, ops.Tensor):
              try:
                  shape = constant_op._tensor_shape_tensor_conversion_function(tensor_shape.TensorShape(shape))
              except (TypeError, ValueError):
                  shape = ops.convert_to_tensor(shape, dtype=dtypes.int32)
  
          if not shape._shape_tuple():
              shape = reshape(shape, [-1])  # Ensure it's a vector
  
          try:
              output = constant(alpha_value, shape=shape, dtype=alpha_dtype, name=name)
  
          except (TypeError, ValueError):
              output = fill(shape, constant(alpha_value, dtype=alpha_dtype), name=name)
  
          if output.dtype.base_dtype != alpha_dtype:
              raise AssertionError("Dtypes do not corresponds: %s and %s" % (output.dtype.base_dtype, alpha_dtype))
  
          return output
docs: Creates a tensor with all elements set to `alpha_value`.
      This operation returns a tensor of type `dtype` with shape `shape` and all
      elements set to alpha.
  
      Parameters
      ----------
      shape: A list of integers, a tuple of integers, or a 1-D `Tensor` of type `int32`.
          The shape of the desired tensor
      alpha_value: `float32`, `float64`, `int8`, `uint8`, `int16`, `uint16`, int32`, `int64`
          The value used to fill the resulting `Tensor`.
      name: str
          A name for the operation (optional).
  
      Returns
      -------
      A `Tensor` with all elements set to alpha.
  
      Examples
      --------
      >>> tl.alphas([2, 3], tf.int32)  # [[alpha, alpha, alpha], [alpha, alpha, alpha]]