class_docstr: StudioML policy for adjusting experiment dependencies
  to specific execution environment and required resources.
code: def generate(self, resources_needed):
      if freeze is None:
          raise ValueError(
              "freeze operation is not available for StudioDependencyPolicy"
          )
  
      needs_gpu = self._needs_gpu(resources_needed)
      packages = freeze.freeze()
      result = []
      for pkg in packages:
          if self._is_special_reference(pkg):
              # git repo or directly installed package
              result.append(pkg)
          elif "==" in pkg:
              # pypi package
              pkey = re.search(r"^.*?(?=\=\=)", pkg).group(0)
              pversion = re.search(r"(?<=\=\=).*\Z", pkg).group(0)
  
              if needs_gpu and (
                  pkey == "tensorflow" or pkey == "tf-nightly"
              ):
                  pkey = pkey + "-gpu"
  
              # TODO add installation logic for torch
              result.append(pkey + "==" + pversion)
      return result
  
imports: import re
  from studio.dependencies_policies.dependencies_policy import DependencyPolicy
label_desc: Use image node_id 1 for calling the StudioDependencyPolicy obj's underlying member method code with example usage: obj.generate(resources_needed) and returns: result