code: def _normalize_find_and_replace_input(
      input_column_dtype: DtypeObj,
      col_to_normalize: Union[ColumnBase, list],
  ) -> ColumnBase:
      normalized_column = column.as_column(
          col_to_normalize,
          dtype=input_column_dtype
          if len(col_to_normalize) <= 0
          else None,
      )
      col_to_normalize_dtype = normalized_column.dtype
      if isinstance(col_to_normalize, list):
          if normalized_column.null_count == len(normalized_column):
              normalized_column = normalized_column.astype(
                  input_column_dtype
              )
          col_to_normalize_dtype = min_column_type(
              normalized_column, input_column_dtype
          )
          # Scalar case
          if len(col_to_normalize) == 1:
              if cudf._lib.scalar._is_null_host_scalar(
                  col_to_normalize[0]
              ):
                  return normalized_column.astype(input_column_dtype)
              if np.isinf(col_to_normalize[0]):
                  return normalized_column
              col_to_normalize_casted = np.array(
                  col_to_normalize[0]
              ).astype(input_column_dtype)
  
              if not np.isnan(col_to_normalize_casted) and (
                  col_to_normalize_casted != col_to_normalize[0]
              ):
                  raise TypeError(
                      f"Cannot safely cast non-equivalent "
                      f"{col_to_normalize[0]} "
                      f"to {input_column_dtype.name}"
                  )
              else:
                  col_to_normalize_dtype = input_column_dtype
      elif hasattr(col_to_normalize, "dtype"):
          col_to_normalize_dtype = col_to_normalize.dtype
      else:
          raise TypeError(
              f"Type {type(col_to_normalize)} not supported"
          )
  
      if (
          col_to_normalize_dtype.kind == "f"
          and input_column_dtype.kind in {"i", "u"}
      ) or (col_to_normalize_dtype.num > input_column_dtype.num):
          raise TypeError(
              f"Potentially unsafe cast for non-equivalent "
              f"{col_to_normalize_dtype.name} "
              f"to {input_column_dtype.name}"
          )
      return normalized_column.astype(input_column_dtype)
  
imports: from __future__ import annotations
  from typing import Any, Callable, Mapping, Optional, Sequence, Tuple, Union, cast
  import cupy
  import numpy
  import pandas
  from typing_extensions import Self
  import cudf
  from cudf import _lib
  from cudf._lib.types import size_type_dtype
  from cudf._typing import ColumnBinaryOperand, ColumnLike, Dtype, DtypeObj, ScalarLike
  from cudf.api.types import is_bool_dtype, is_float_dtype, is_integer, is_integer_dtype, is_scalar
  from cudf.core.buffer import Buffer, cuda_array_interface_wrapper
  from cudf.core.column import ColumnBase, as_column, build_column, column, full, string
  from cudf.core.dtypes import CategoricalDtype
  from cudf.core.mixins import BinaryOperand
  from cudf.utils.dtypes import NUMERIC_TYPES, min_column_type, min_signed_type, np_dtypes_to_pandas_dtypes, numeric_normalize_types
  from .numerical_base import NumericalBaseColumn
label_desc: Use image node_id 1 for calling a global function with example usage: _normalize_find_and_replace_input(input_column_dtype, col_to_normalize) and returns: normalized_column, normalized_column, normalized_column