code: def init_environment(self, echo):
      # The implementation optimizes for latency to ensure as many operations can
      # be turned into cheap no-ops as feasible. Otherwise, we focus on maintaining
      # a balance between latency and maintainability of code without re-implementing
      # the internals of Micromamba and Pip.
  
      # TODO: Introduce verbose logging
      #       https://github.com/Netflix/metaflow/issues/1494
  
      def environments(type_):
          seen = set()
          for step in self.flow:
              environment = self.get_environment(step)
              if (
                  type_ in environment
                  and environment["id_"] not in seen
              ):
                  seen.add(environment["id_"])
                  for platform in environment[type_]["platforms"]:
                      yield environment["id_"], {
                          **{
                              k: v
                              for k, v in environment[type_].items()
                              if k != "platforms"
                          },
                          **{"platform": platform},
                      }
  
      def solve(id_, environment, type_):
          # Cached solve - should be quick!
          platform = environment["platform"]
          return (
              id_,
              (
                  self.read_from_environment_manifest(
                      [id_, platform, type_]
                  )
                  or self.write_to_environment_manifest(
                      [id_, platform, type_],
                      self.solvers[type_].solve(id_, **environment),
                  )
              ),
              environment["python"],
              platform,
          )
  
      def cache(storage, results, type_):
          def _path(url, local_path):
              # Special handling for VCS packages
              if url.startswith("git+"):
                  base, _ = os.path.split(urlparse(url).path)
                  _, file = os.path.split(local_path)
                  prefix = url.split("@")[-1]
                  return urlparse(url).netloc + os.path.join(
                      unquote(base), prefix, file
                  )
              else:
                  return urlparse(url).netloc + urlparse(url).path
  
          local_packages = {
              url: {
                  # Path to package in datastore.
                  "path": _path(
                      url, local_path
                  ),  # urlparse(url).netloc + urlparse(url).path,
                  # Path to package on local disk.
                  "local_path": local_path,
              }
              for result in results
              for url, local_path in self.solvers[type_]
              .metadata(*result)
              .items()
          }
          dirty = set()
          # Prune list of packages to cache.
  
          _meta = copy.deepcopy(local_packages)
          for id_, packages, _, _ in results:
              for package in packages:
                  if package.get("path"):
                      # Cache only those packages that manifest is unaware of
                      local_packages.pop(package["url"], None)
                  else:
                      package["path"] = _meta[package["url"]]["path"]
                      dirty.add(id_)
  
          list_of_path_and_filehandle = [
              (
                  package["path"],
                  # Lazily fetch package from the interweb if needed.
                  LazyOpen(
                      package["local_path"],
                      "rb",
                      url,
                  ),
              )
              for url, package in local_packages.items()
          ]
          storage.save_bytes(
              list_of_path_and_filehandle,
              len_hint=len(list_of_path_and_filehandle),
          )
          for id_, packages, _, platform in results:
              if id_ in dirty:
                  self.write_to_environment_manifest(
                      [id_, platform, type_], packages
                  )
  
      # First resolve environments through Conda, before PyPI.
      echo("Bootstrapping virtual environment(s) ...")
      for solver in ["conda", "pypi"]:
          with ThreadPoolExecutor() as executor:
              results = list(
                  executor.map(
                      lambda x: solve(*x, solver), environments(solver)
                  )
              )
          _ = list(
              map(lambda x: self.solvers[solver].download(*x), results)
          )
          with ThreadPoolExecutor() as executor:
              _ = list(
                  executor.map(
                      lambda x: self.solvers[solver].create(*x), results
                  )
              )
          if self.datastore_type not in ["local"]:
              # Cache packages only when a remote datastore is in play.
              storage = self.datastore(
                  _datastore_packageroot(self.datastore, self.echo)
              )
              cache(storage, results, solver)
      echo("Virtual environment(s) bootstrapped!")
  
imports: import copy
  import errno
  import fcntl
  import functools
  import io
  import json
  import os
  import sys
  import time
  from concurrent.futures import ThreadPoolExecutor
  from hashlib import sha256
  from io import BufferedIOBase
  from itertools import chain
  from urllib.parse import unquote, urlparse
  import requests
  from metaflow.exception import MetaflowException
  from metaflow.metaflow_config import get_pinned_conda_libs
  from metaflow.metaflow_environment import MetaflowEnvironment
  from metaflow.metaflow_profile import profile
  from .None import MAGIC_FILE, _datastore_packageroot
  from .utils import conda_platform
label_desc: Use image node_id 5 for calling the CondaEnvironment obj's underlying member method code with example usage: obj.init_environment(echo) and returns: id_,  unknown,  environment,  platform, unknown, unknown