You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: variable.rs
    path of file: ./repos/candle/candle-core/src
    
    the code of the file until where you have to start completion: // Variables are wrappers around tensors that can be modified, they are typically used for holding
// weights and being modified by gradient descent.
// We do not expose a public way to create variables as this would break the invariant that the
// tensor within a variable is actually with `is_variable` set to `true`.
use crate::{DType, Device, Error, Result, Shape, Tensor};

/// A variable is a wrapper around a tensor, however variables can have their content modified
/// whereas tensors are immutable.
#[derive(Clone, Debug)]
pub struct Var(Tensor);

impl std::fmt::Display for Var {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        std::fmt::Display::fmt(&self.0, f)
    }
}

impl std::ops::Deref for Var {
    type T
    
    pub fn set(&self, src: &Tensor) -> Result<()> {
        if self.same_storage(src) {
            let msg = "cannot set a variable to a tensor that is derived from its value";
            Err(Error::CannotSetVar { msg }.bt())?
        }
        let (mut dst, layout) = self.storage_mut_and_layout();
        if !layout.is_contiguous() {
            let msg = "cannot set a non-contiguous variable";
            Err(Error::CannotSetVar { msg }.bt())?
        }
        let (src, src_l) = src.storage_and_layout();
        if layout.shape() != src_l.shape() {
            Err(Error::ShapeMismatchBinaryOp {
                lhs: layout.shape().clone(),
                rhs: src_l.shape().clone(),
                op: "set",
            }
            .bt())?
        }
        src.copy_strided_src(&mut dst, layout.start_offset(), src_l)?;
        Ok(())
    }