parameters: (value: Tensor or Number or tuple or list or Any)
docstring: Converts `value` to a `numpy.ndarray` where value must be a `Tensor`, backend tensor or tensor-like.
      If `value` is a `phi.math.Tensor`, this is equal to calling `phi.math.Tensor.numpy()`.
  
      *Note*: Using this function breaks the autograd chain. The returned tensor is not differentiable.
      To get a differentiable tensor, use `Tensor.native()` instead.
  
      Transposes the underlying tensor to match the name order and adds singleton dimensions for new dimension names.
      If a dimension of the tensor is not listed in `order`, a `ValueError` is raised.
  
      If `value` is a NumPy array, it may be returned directly.
  
      Returns:
          NumPy representation of `value`
  
      Raises:
          ValueError if the tensor cannot be transposed to match target_shape
docstring_summary: Converts `value` to a `numpy.ndarray` where value must be a `Tensor`, backend tensor or tensor-like.
      If `value` is a `phi.math.Tensor`, this is equal to calling `phi.math.Tensor.numpy()`.
docstring_tokens: Converts value to a numpy . ndarray where value must be a Tensor backend tensor or tensor - like . If value is a phi . math . Tensor this is equal to calling phi . math . Tensor . numpy () .
function: def numpy(value: Tensor or Number or tuple or list or Any):
      """
      Converts `value` to a `numpy.ndarray` where value must be a `Tensor`, backend tensor or tensor-like.
      If `value` is a `phi.math.Tensor`, this is equal to calling `phi.math.Tensor.numpy()`.
  
      *Note*: Using this function breaks the autograd chain. The returned tensor is not differentiable.
      To get a differentiable tensor, use `Tensor.native()` instead.
  
      Transposes the underlying tensor to match the name order and adds singleton dimensions for new dimension names.
      If a dimension of the tensor is not listed in `order`, a `ValueError` is raised.
  
      If `value` is a NumPy array, it may be returned directly.
  
      Returns:
          NumPy representation of `value`
  
      Raises:
          ValueError if the tensor cannot be transposed to match target_shape
      """
      if isinstance(value, Tensor):
          return value.numpy()
      else:
          backend = choose_backend(value)
          return backend.numpy(value)
function_tokens: def numpy ( value : Tensor or Number or tuple or list or Any ) : if isinstance ( value , Tensor ) : return value . numpy ( ) else : backend = choose_backend ( value ) return backend . numpy ( value )