code: def read_pandas(
      reader,
      urlpath,
      blocksize="default",
      lineterminator=None,
      compression="infer",
      sample=256000,
      sample_rows=10,
      enforce=False,
      assume_missing=False,
      storage_options=None,
      include_path_column=False,
      **kwargs,
  ):
      reader_name = reader.__name__
      if lineterminator is not None and len(lineterminator) == 1:
          kwargs["lineterminator"] = lineterminator
      else:
          lineterminator = "\n"
      if include_path_column and isinstance(include_path_column, bool):
          include_path_column = "path"
      if "index" in kwargs or (
          "index_col" in kwargs and kwargs.get("index_col") is not False
      ):
          raise ValueError(
              "Keywords 'index' and 'index_col' not supported, except for "
              "'index_col=False'. Use dd.{reader_name}(...).set_index('my-index') instead"
          )
      for kw in ["iterator", "chunksize"]:
          if kw in kwargs:
              raise ValueError(
                  f"{kw} not supported for dd.{reader_name}"
              )
      if kwargs.get("nrows", None):
          raise ValueError(
              "The 'nrows' keyword is not supported by "
              "`dd.{0}`. To achieve the same behavior, it's "
              "recommended to use `dd.{0}(...)."
              "head(n=nrows)`".format(reader_name)
          )
      if isinstance(kwargs.get("skiprows"), int):
          lastskiprow = firstrow = kwargs.get("skiprows")
      elif kwargs.get("skiprows") is None:
          lastskiprow = firstrow = 0
      else:
          # When skiprows is a list, we expect more than max(skiprows) to
          # be included in the sample. This means that [0,2] will work well,
          # but [0, 440] might not work.
          skiprows = set(kwargs.get("skiprows"))
          lastskiprow = max(skiprows)
          # find the firstrow that is not skipped, for use as header
          firstrow = min(set(range(len(skiprows) + 1)) - set(skiprows))
      if isinstance(kwargs.get("header"), list):
          raise TypeError(
              f"List of header rows not supported for dd.{reader_name}"
          )
      if (
          isinstance(kwargs.get("converters"), dict)
          and include_path_column
      ):
          path_converter = kwargs.get("converters").get(
              include_path_column, None
          )
      else:
          path_converter = None
  
      # If compression is "infer", inspect the (first) path suffix and
      # set the proper compression option if the suffix is recongnized.
      if compression == "infer":
          # Translate the input urlpath to a simple path list
          paths = get_fs_token_paths(
              urlpath, mode="rb", storage_options=storage_options
          )[2]
  
          # Check for at least one valid path
          if len(paths) == 0:
              raise OSError(f"{urlpath} resolved to no files")
  
          # Infer compression from first path
          compression = infer_compression(paths[0])
  
      if blocksize == "default":
          blocksize = AUTO_BLOCKSIZE
      if isinstance(blocksize, str):
          blocksize = parse_bytes(blocksize)
      if blocksize and compression:
          # NONE of the compressions should use chunking
          warn(
              "Warning %s compression does not support breaking apart files\n"
              "Please ensure that each individual file can fit in memory and\n"
              "use the keyword ``blocksize=None to remove this message``\n"
              "Setting ``blocksize=None``" % compression
          )
          blocksize = None
      if compression not in compr:
          raise NotImplementedError(
              "Compression format %s not installed" % compression
          )
      if (
          blocksize
          and sample
          and blocksize < sample
          and lastskiprow != 0
      ):
          warn(
              "Unexpected behavior can result from passing skiprows when\n"
              "blocksize is smaller than sample size.\n"
              "Setting ``sample=blocksize``"
          )
          sample = blocksize
      b_lineterminator = lineterminator.encode()
      b_out = read_bytes(
          urlpath,
          delimiter=b_lineterminator,
          blocksize=blocksize,
          sample=sample,
          compression=compression,
          include_path=include_path_column,
          **(storage_options or {}),
      )
  
      if include_path_column:
          b_sample, values, paths = b_out
          path = (include_path_column, path_converter)
      else:
          b_sample, values = b_out
          path = None
  
      if not isinstance(values[0], (tuple, list)):
          values = [values]
      # If we have not sampled, then use the first row of the first values
      # as a representative sample.
      if b_sample is False and len(values[0]):
          b_sample = values[0][0].compute()
  
      # Get header row, and check that sample is long enough. If the file
      # contains a header row, we need at least 2 nonempty rows + the number of
      # rows to skip.
      names = kwargs.get("names", None)
      header = kwargs.get("header", "infer" if names is None else None)
      need = 1 if header is None else 2
      if isinstance(header, int):
          firstrow += header
      if kwargs.get("comment"):
          # if comment is provided, step through lines of b_sample and strip out comments
          parts = []
          for part in b_sample.split(b_lineterminator):
              split_comment = part.decode().split(kwargs.get("comment"))
              if len(split_comment) > 1:
                  # if line starts with comment, don't include that line in parts.
                  if len(split_comment[0]) > 0:
                      parts.append(split_comment[0].strip().encode())
              else:
                  parts.append(part)
              if len(parts) > need:
                  break
      else:
          parts = b_sample.split(
              b_lineterminator, max(lastskiprow + need, firstrow + need)
          )
  
      # If the last partition is empty, don't count it
      nparts = 0 if not parts else len(parts) - int(not parts[-1])
  
      if (
          sample is not False
          and nparts < lastskiprow + need
          and len(b_sample) >= sample
      ):
          raise ValueError(
              "Sample is not large enough to include at least one "
              "row of data. Please increase the number of bytes "
              "in `sample` in the call to `read_csv`/`read_table`"
          )
  
      header = (
          b"" if header is None else parts[firstrow] + b_lineterminator
      )
  
      # Use sample to infer dtypes and check for presence of include_path_column
      head_kwargs = kwargs.copy()
      head_kwargs.pop("skipfooter", None)
      if head_kwargs.get("engine") == "pyarrow":
          # Use c engine to infer since Arrow engine does not support nrows
          head_kwargs["engine"] = "c"
      try:
          head = reader(
              BytesIO(b_sample), nrows=sample_rows, **head_kwargs
          )
      except pd.errors.ParserError as e:
          if "EOF" in str(e):
              raise ValueError(
                  "EOF encountered while reading header. \n"
                  "Pass argument `sample_rows` and make sure the value of `sample` "
                  "is large enough to accommodate that many rows of data"
              ) from e
          raise
      if include_path_column and (include_path_column in head.columns):
          raise ValueError(
              "Files already contain the column name: %s, so the "
              "path column cannot use this name. Please set "
              "`include_path_column` to a unique name."
              % include_path_column
          )
  
      specified_dtypes = kwargs.get("dtype", {})
      if specified_dtypes is None:
          specified_dtypes = {}
      # If specified_dtypes is a single type, then all columns were specified
      if assume_missing and isinstance(specified_dtypes, dict):
          # Convert all non-specified integer columns to floats
          for c in head.columns:
              if (
                  is_integer_dtype(head[c].dtype)
                  and c not in specified_dtypes
              ):
                  head[c] = head[c].astype(float)
  
      values = [
          [list(dsk.dask.values()) for dsk in block] for block in values
      ]
  
      return text_blocks_to_pandas(
          reader,
          values,
          header,
          head,
          kwargs,
          enforce=enforce,
          specified_dtypes=specified_dtypes,
          path=path,
          blocksize=blocksize,
          urlpath=urlpath,
      )
  
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 8 for calling a global function with example usage: read_pandas(reader, urlpath, blocksize, lineterminator, compression, sample, sample_rows, enforce, assume_missing, storage_options, include_path_column) and returns: text_blocks_to_pandas