code: def pyarrow_table_to_torch_dict(
      tt: pa.Table, strict: bool = True
  ) -> NestedDict:
      return {
          col: from_pyarrow_to_torch_tensor(tt[col], strict)
          for col in tt.column_names
      }
  
imports: from contextlib import contextmanager
  from typing import Dict, Generator, List, Optional, Union
  import numpy
  import pandas
  import pyarrow
  import pyarrow.parquet
  import torch
  from numpy.typing import NDArray
  from pyarrow.dataset import get_partition_keys
  from fairseq2.data import CString
  from fairseq2.data.data_pipeline import DataPipeline, DataPipelineBuilder, read_sequence
label_desc: Use image node_id 4 for calling a global function with example usage: pyarrow_table_to_torch_dict(tt, strict) and returns: unknown