code: def text_blocks_to_pandas(
      reader,
      block_lists,
      header,
      head,
      kwargs,
      enforce=False,
      specified_dtypes=None,
      path=None,
      blocksize=None,
      urlpath=None,
  ):
      """Convert blocks of bytes to a dask.dataframe
  
      This accepts a list of lists of values of bytes where each list corresponds
      to one file, and the value of bytes concatenate to comprise the entire
      file, in order.
  
      Parameters
      ----------
      reader : callable
          ``pd.read_csv`` or ``pd.read_table``.
      block_lists : list of lists of delayed values of bytes
          The lists of bytestrings where each list corresponds to one logical file
      header : bytestring
          The header, found at the front of the first file, to be prepended to
          all blocks
      head : pd.DataFrame
          An example Pandas DataFrame to be used for metadata.
      kwargs : dict
          Keyword arguments to pass down to ``reader``
      path : tuple, optional
          A tuple containing column name for path and the path_converter if provided
  
      Returns
      -------
      A dask.dataframe
      """
      dtypes = head.dtypes.to_dict()
      # dtypes contains only instances of CategoricalDtype, which causes issues
      # in coerce_dtypes for non-uniform categories across partitions.
      # We will modify `dtype` (which is inferred) to
      # 1. contain instances of CategoricalDtypes for user-provided types
      # 2. contain 'category' for data inferred types
      categoricals = head.select_dtypes(include=["category"]).columns
  
      if isinstance(specified_dtypes, Mapping):
          known_categoricals = [
              k
              for k in categoricals
              if isinstance(specified_dtypes.get(k), CategoricalDtype)
              and specified_dtypes.get(k).categories is not None
          ]
          unknown_categoricals = categoricals.difference(
              known_categoricals
          )
      else:
          unknown_categoricals = categoricals
  
      # Fixup the dtypes
      for k in unknown_categoricals:
          dtypes[k] = "category"
  
      columns = list(head.columns)
  
      blocks = tuple(flatten(block_lists))
      # Create mask of first blocks from nested block_lists
      is_first = tuple(block_mask(block_lists))
      is_last = tuple(block_mask_last(block_lists))
  
      if path:
          colname, path_converter = path
          paths = [b[1].path for b in blocks]
          if path_converter:
              paths = [path_converter(p) for p in paths]
          head = head.assign(
              **{
                  colname: pd.Categorical.from_codes(
                      np.zeros(len(head), dtype=int), set(paths)
                  )
              }
          )
          path = (colname, paths)
  
      if len(unknown_categoricals):
          head = clear_known_categories(head, cols=unknown_categoricals)
  
      # Define parts
      parts = []
      colname, paths = path or (None, None)
      for i in range(len(blocks)):
          parts.append(
              [
                  blocks[i],
                  paths[i] if paths else None,
                  is_first[i],
                  is_last[i],
              ]
          )
  
      # Construct the output collection with from_map
      return from_map(
          CSVFunctionWrapper(
              columns,
              None,
              colname,
              head,
              header,
              reader,
              dtypes,
              enforce,
              kwargs,
          ),
          parts,
          meta=head,
          label="read-csv",
          token=tokenize(
              reader, urlpath, columns, enforce, head, blocksize
          ),
          enforce_metadata=False,
          produces_tasks=True,
      )
  
imports: from __future__ import annotations
  import os
  from collections.abc import Mapping
  from io import BytesIO
  from warnings import catch_warnings, simplefilter, warn
  import numpy
  import pandas
  from fsspec.compression import compr
  from fsspec.core import get_fs_token_paths
  from fsspec.core import open
  from fsspec.core import open_files
  from fsspec.utils import infer_compression
  from pandas.api.types import CategoricalDtype, is_datetime64_any_dtype, is_float_dtype, is_integer_dtype, is_object_dtype
  from dask.base import tokenize
  from dask.bytes import read_bytes
  from dask.core import flatten
  from dask.dataframe.backends import dataframe_creation_dispatch
  from dask.dataframe.io.io import from_map
  from dask.dataframe.io.utils import DataFrameIOFunction
  from dask.dataframe.utils import clear_known_categories
  from dask.delayed import delayed
  from dask.utils import asciitable, parse_bytes
  from dask.dataframe.core import _Frame
label_desc: Use image node_id 3 for calling a global function with example usage: text_blocks_to_pandas(reader, block_lists, header, head, kwargs, enforce, specified_dtypes, path, blocksize, urlpath) and returns: from_map