While trying to import documents from google storage bucket using this function we are getting an HTTP:500 error, we are trying to push the documents into the train queue of the specified processor

def import_docs_to_train(bucket_name, gcs_uri, processor_id, project_id, location):
    print(" In Importing documents to train the model function...")
    storage_client = storage.Client()
    bucket = storage_client.bucket(bucket_name)
    dataset_gcs_uri = f'gs://{BUCKET_NAME_OUTPUT}/'
    
    if True:
        print(f"GCS URI of the JSON file: {json_file_gcs_uri}")
        print(f"GCS URI of the input documents: {gcs_uri}")
        client = documentai.DocumentServiceClient()

        batch_input_config = BatchDocumentsInputConfig(
            gcs_prefix=GcsPrefix(gcs_uri_prefix=gcs_uri)
        )

        batch_documents_import_configs = ImportDocumentsRequest.BatchDocumentsImportConfig(
            batch_input_config=batch_input_config
        )

        batch_inputs=[]
        batch_documents_import_configs =   ImportDocumentsRequest.BatchDocumentsImportConfig(batch_input_config=batch_input_config)
        
        batch_documents_import_configs.dataset_split = "DATASET_SPLIT_UNASSIGNED"
        batch_inputs.append(batch_documents_import_configs)
        #update_dataset(dataset)
        doc_request = ImportDocumentsRequest(
        
            dataset=dataset_gcs_uri,
            batch_documents_import_configs=batch_inputs
        
        )

Getting the following error:

raise exceptions.from_grpc_error(exc) from exc
google.api_core.exceptions.InvalidArgument: 400 Invalid resource field value in the request. [reason: "RESOURCE_PROJECT_INVALID"
domain: "googleapis.com"
metadata {
  key: "service"
  value: "documentai.googleapis.com"
}
metadata {
  key: "method"
  value: "google.cloud.documentai.v1beta3.DocumentService.ImportDocuments"
}
]