code: def julia_code(expr, assign_to=None, **settings):
      r"""Converts `expr` to a string of Julia code.
  
      Parameters
      ==========
  
      expr : Expr
          A SymPy expression to be converted.
      assign_to : optional
          When given, the argument is used as the name of the variable to which
          the expression is assigned.  Can be a string, ``Symbol``,
          ``MatrixSymbol``, or ``Indexed`` type.  This can be helpful for
          expressions that generate multi-line statements.
      precision : integer, optional
          The precision for numbers such as pi  [default=16].
      user_functions : dict, optional
          A dictionary where keys are ``FunctionClass`` instances and values are
          their string representations.  Alternatively, the dictionary value can
          be a list of tuples i.e. [(argument_test, cfunction_string)].  See
          below for examples.
      human : bool, optional
          If True, the result is a single string that may contain some constant
          declarations for the number symbols.  If False, the same information is
          returned in a tuple of (symbols_to_declare, not_supported_functions,
          code_text).  [default=True].
      contract: bool, optional
          If True, ``Indexed`` instances are assumed to obey tensor contraction
          rules and the corresponding nested loops over indices are generated.
          Setting contract=False will not generate loops, instead the user is
          responsible to provide values for the indices in the code.
          [default=True].
      inline: bool, optional
          If True, we try to create single-statement code instead of multiple
          statements.  [default=True].
  
      Examples
      ========
  
      >>> from sympy import julia_code, symbols, sin, pi
      >>> x = symbols('x')
      >>> julia_code(sin(x).series(x).removeO())
      'x .^ 5 / 120 - x .^ 3 / 6 + x'
  
      >>> from sympy import Rational, ceiling
      >>> x, y, tau = symbols("x, y, tau")
      >>> julia_code((2*tau)**Rational(7, 2))
      '8 * sqrt(2) * tau .^ (7 // 2)'
  
      Note that element-wise (Hadamard) operations are used by default between
      symbols.  This is because its possible in Julia to write "vectorized"
      code.  It is harmless if the values are scalars.
  
      >>> julia_code(sin(pi*x*y), assign_to="s")
      's = sin(pi * x .* y)'
  
      If you need a matrix product "*" or matrix power "^", you can specify the
      symbol as a ``MatrixSymbol``.
  
      >>> from sympy import Symbol, MatrixSymbol
      >>> n = Symbol('n', integer=True, positive=True)
      >>> A = MatrixSymbol('A', n, n)
      >>> julia_code(3*pi*A**3)
      '(3 * pi) * A ^ 3'
  
      This class uses several rules to decide which symbol to use a product.
      Pure numbers use "*", Symbols use ".*" and MatrixSymbols use "*".
      A HadamardProduct can be used to specify componentwise multiplication ".*"
      of two MatrixSymbols.  There is currently there is no easy way to specify
      scalar symbols, so sometimes the code might have some minor cosmetic
      issues.  For example, suppose x and y are scalars and A is a Matrix, then
      while a human programmer might write "(x^2*y)*A^3", we generate:
  
      >>> julia_code(x**2*y*A**3)
      '(x .^ 2 .* y) * A ^ 3'
  
      Matrices are supported using Julia inline notation.  When using
      ``assign_to`` with matrices, the name can be specified either as a string
      or as a ``MatrixSymbol``.  The dimensions must align in the latter case.
  
      >>> from sympy import Matrix, MatrixSymbol
      >>> mat = Matrix([[x**2, sin(x), ceiling(x)]])
      >>> julia_code(mat, assign_to='A')
      'A = [x .^ 2 sin(x) ceil(x)]'
  
      ``Piecewise`` expressions are implemented with logical masking by default.
      Alternatively, you can pass "inline=False" to use if-else conditionals.
      Note that if the ``Piecewise`` lacks a default term, represented by
      ``(expr, True)`` then an error will be thrown.  This is to prevent
      generating an expression that may not evaluate to anything.
  
      >>> from sympy import Piecewise
      >>> pw = Piecewise((x + 1, x > 0), (x, True))
      >>> julia_code(pw, assign_to=tau)
      'tau = ((x > 0) ? (x + 1) : (x))'
  
      Note that any expression that can be generated normally can also exist
      inside a Matrix:
  
      >>> mat = Matrix([[x**2, pw, sin(x)]])
      >>> julia_code(mat, assign_to='A')
      'A = [x .^ 2 ((x > 0) ? (x + 1) : (x)) sin(x)]'
  
      Custom printing can be defined for certain types by passing a dictionary of
      "type" : "function" to the ``user_functions`` kwarg.  Alternatively, the
      dictionary value can be a list of tuples i.e., [(argument_test,
      cfunction_string)].  This can be used to call a custom Julia function.
  
      >>> from sympy import Function
      >>> f = Function('f')
      >>> g = Function('g')
      >>> custom_functions = {
      ...   "f": "existing_julia_fcn",
      ...   "g": [(lambda x: x.is_Matrix, "my_mat_fcn"),
      ...         (lambda x: not x.is_Matrix, "my_fcn")]
      ... }
      >>> mat = Matrix([[1, x]])
      >>> julia_code(f(x) + g(x) + g(mat), user_functions=custom_functions)
      'existing_julia_fcn(x) + my_fcn(x) + my_mat_fcn([1 x])'
  
      Support for loops is provided through ``Indexed`` types. With
      ``contract=True`` these expressions will be turned into loops, whereas
      ``contract=False`` will just print the assignment expression that should be
      looped over:
  
      >>> from sympy import Eq, IndexedBase, Idx
      >>> len_y = 5
      >>> y = IndexedBase('y', shape=(len_y,))
      >>> t = IndexedBase('t', shape=(len_y,))
      >>> Dy = IndexedBase('Dy', shape=(len_y-1,))
      >>> i = Idx('i', len_y-1)
      >>> e = Eq(Dy[i], (y[i+1]-y[i])/(t[i+1]-t[i]))
      >>> julia_code(e.rhs, assign_to=e.lhs, contract=False)
      'Dy[i] = (y[i + 1] - y[i]) ./ (t[i + 1] - t[i])'
      """
      return JuliaCodePrinter(settings).doprint(expr, assign_to)
  
imports: from __future__ import annotations
  from typing import Any
  from sympy.core import Mul, Pow, S, Rational
  from sympy.core.mul import _keep_coeff
  from sympy.core.numbers import equal_valued
  from sympy.printing.codeprinter import CodePrinter
  from sympy.printing.precedence import precedence, PRECEDENCE
  from re import search
label_desc: Use image node_id 1 for calling a global function with example usage: julia_code(expr, assign_to) and returns: JuliaCodePrinter