class_docstr: Implementation. Captures batches in JSON format, returns
  also in JSON format.
docstr: Extracts the data from the JSON object
code: def _from_json(self, data):
      """
      Extracts the data from the JSON object
      """
      rows = (data.get("data") or data.get("body") or data)["instances"]
      if isinstance(rows[0], dict):
          for row_i, row in enumerate(rows):
              if list(row.keys()) == ["b64"]:
                  rows[row_i] = b64decode(row["b64"])
              else:
                  for col, col_value in row.items():
                      if isinstance(col_value, dict) and list(
                          col_value.keys()
                      ) == ["b64"]:
                          row[col] = b64decode(col_value["b64"])
      return rows
  
file_path: serve/ts/torch_handler/request_envelope/json.py
imports: import json
  from base64 import b64decode
  from itertools import chain
  from .base import BaseEnvelope
label_desc: Use image node_id 4 for calling the JSONEnvelope obj's underlying member method code with example usage: obj._from_json(data) and returns: rows