How to use the a2ml.api.utils.local_fsclient.LocalFSClient function in a2ml

def download_file(self, path, local_path):
        from .local_fsclient import LocalFSClient
        import boto3
        try:
            from urllib.parse import urlparse
        except ImportError:
            from urlparse import urlparse

        if path.startswith("http:") or path.startswith("https:"):
            s3_path = self._get_relative_path(local_path)

            uri = urlparse(path)

            with LocalFSClient().save_atomic(uri.path) as temp_file:
                LocalFSClient().downloadFile(path, temp_file)
                self._s3_upload_file(temp_file, s3_path)

                #FSClient().waitForFile(local_path, if_wait_for_file=True, num_tries=3000, interval_sec=20)
            # with FSClient().open(path, "rb", encoding=None) as fd:
            #     self.client.upload_fileobj(fd, Bucket=self.s3BucketName, Key=s3_path)
        else:
            path = self._get_relative_path(path)
            LocalFSClient().create_parent_folder(local_path)
            self.client.download_file(
                Bucket=self.s3BucketName, Key=path, Filename=local_path)

def save_atomic(path, move_file=True):
    localClient = LocalFSClient()

    with localClient.save_atomic(path) as local_path:
        yield local_path

        if move_file:
            client = _get_fsclient_bypath(path)
            client.move_file(local_path, path)

def read_text_file(self, path):
        from .local_fsclient import LocalFSClient

        with LocalFSClient().save_atomic(path) as local_tmp_path:
            self.download_file(path, local_tmp_path)
            return LocalFSClient().read_text_file(local_tmp_path)

def _get_remote_model_features(self, remote_run):
        from  a2ml.api.utils import fsclient, local_fsclient
        import pandas as pd

        model_features = None
        target_categories = None

        temp_dir = local_fsclient.LocalFSClient().get_temp_folder()
        try:
            file_name = 'scoring_file_v_1_0_0.py'
            remote_run.download_file('outputs/%s'%file_name, os.path.join(temp_dir, file_name))
            text = fsclient.read_text_file(os.path.join(temp_dir, file_name))
            to_find = "input_sample ="
            start = text.find(to_find)
            if start > 0:
                end = text.find("\n", start)
                if end > start:
                    code_to_run = text[start+len(to_find):end]

                    input_sample = eval(code_to_run)
                    model_features = input_sample.columns.tolist()
        except Exception as e:
            self.ctx.log('Cannot get columns from remote model.Use original columns from predicted data: %s'%e)

def read_text_file(self, path):
        from .local_fsclient import LocalFSClient

        with LocalFSClient().save_atomic(path) as local_tmp_path:
            self.download_file(path, local_tmp_path)
            return LocalFSClient().read_text_file(local_tmp_path)

if path.startswith("http:") or path.startswith("https:"):
            s3_path = self._get_relative_path(local_path)

            uri = urlparse(path)

            with LocalFSClient().save_atomic(uri.path) as temp_file:
                LocalFSClient().downloadFile(path, temp_file)
                self._s3_upload_file(temp_file, s3_path)

                #FSClient().waitForFile(local_path, if_wait_for_file=True, num_tries=3000, interval_sec=20)
            # with FSClient().open(path, "rb", encoding=None) as fd:
            #     self.client.upload_fileobj(fd, Bucket=self.s3BucketName, Key=s3_path)
        else:
            path = self._get_relative_path(path)
            LocalFSClient().create_parent_folder(local_path)
            self.client.download_file(
                Bucket=self.s3BucketName, Key=path, Filename=local_path)