code: def init_phases(x: torch.Tensor) -> torch.Tensor:
      r"""
      Generate random phases between 0 and :math:`2\pi`.
  
      .. note::
          This method works on the canonical torch real representation of complex tensors, cf.
          https://pytorch.org/docs/stable/complex_numbers.html
  
      :param x:
          a tensor to initialize
  
      :return:
          tensor with weights set by this initializer
  
      .. seealso ::
          :func:`torch.view_as_real`
      """
      # backwards compatibility
      if x.shape[-1] != 2:
          new_shape = (*x.shape[:-1], -1, 2)
          logger.warning(
              f"The input tensor shape, {tuple(x.shape)}, does not comply with the canonical complex tensor shape, "
              f"(..., 2), cf. https://pytorch.org/docs/stable/complex_numbers.html. We'll try to reshape to {new_shape}",
          )
          x = x.view(*new_shape)
      phases = (
          2 * np.pi * torch.rand_like(torch.view_as_complex(x).real)
      )
      return torch.view_as_real(
          torch.complex(real=phases.cos(), imag=phases.sin())
      ).detach()
  
imports: import functools
  import logging
  import math
  from typing import Any, Optional, Sequence
  import numpy
  import torch
  import torch.nn
  import torch.nn.init
  import torch_ppr.utils
  from class_resolver import FunctionResolver, Hint, HintOrType, OptionalKwargs
  from more_itertools import last
  from torch.nn import functional
  from .text import TextEncoder, text_encoder_resolver
  from .utils import iter_matrix_power, safe_diagonal
  from ..triples import CoreTriplesFactory, TriplesFactory
  from ..typing import Initializer, MappedTriples, OneOrSequence
  from ..utils import compose, get_edge_index, iter_weisfeiler_lehman, upgrade_to_sequence
label_desc: Use image node_id 3 for calling a global function with example usage: init_phases(x) and returns: torch