# Buggy code
def init_ndarray(values, index, columns, dtype=None, copy=False):
    # input must be a ndarray, list, Series, index

    if isinstance(values, ABCSeries):
        if columns is None:
            if values.name is not None:
                columns = [values.name]
        if index is None:
            index = values.index
        else:
            values = values.reindex(index)

        # zero len case (GH #2234)
        if not len(values) and columns is not None and len(columns):
            values = np.empty((0, 1), dtype=object)

    # we could have a categorical type passed or coerced to 'category'
    # recast this to an arrays_to_mgr
    if is_categorical_dtype(getattr(values, "dtype", None)) or is_categorical_dtype(
        dtype
    ):
        if not hasattr(values, "dtype"):
            values = prep_ndarray(values, copy=copy)
            values = values.ravel()
        elif copy:
            values = values.copy()

        index, columns = _get_axes(len(values), 1, index, columns)
        return arrays_to_mgr([values], columns, index, columns, dtype=dtype)
    elif is_extension_array_dtype(values) or is_extension_array_dtype(dtype):
        # GH#19157
        if columns is None:
            columns = [0]
        return arrays_to_mgr([values], columns, index, columns, dtype=dtype)

    # by definition an array here
    # the dtypes will be coerced to a single dtype
    values = prep_ndarray(values, copy=copy)

    if dtype is not None:
        if not is_dtype_equal(values.dtype, dtype):
            try:
                values = values.astype(dtype)
            except Exception as orig:
                # e.g. ValueError when trying to cast object dtype to float64
                raise ValueError(
                    f"failed to cast to '{dtype}' (Exception was: {orig})"
                ) from orig

    index, columns = _get_axes(*values.shape, index=index, columns=columns)
    values = values.T

    # if we don't have a dtype specified, then try to convert objects
    # on the entire block; this is to convert if we have datetimelike's
    # embedded in an object type
    if dtype is None and is_object_dtype(values):
        if values.ndim == 2 and values.shape[0] != 1:
            # transpose and separate blocks

            dvals_list = [maybe_infer_to_datetimelike(row) for row in values]
            for n in range(len(dvals_list)):
                if isinstance(dvals_list[n], np.ndarray):
                    dvals_list[n] = dvals_list[n].reshape(1, -1)

            from pandas.core.internals.blocks import make_block

            # TODO: What about re-joining object columns?
            block_values = [
                make_block(dvals_list[n], placement=[n]) for n in range(len(dvals_list))
            ]

        else:
            datelike_vals = maybe_infer_to_datetimelike(values)
            block_values = [datelike_vals]
    else:
        block_values = [values]

    return create_block_manager_from_blocks(block_values, [columns, index])


# Traceback (TypeError)
TypeError                                 Traceback (most recent call last)
<ipython-input-4-7101cf798aa3> in <module>()
----> 1 df = pd.DataFrame(array_dim2 , dtype='datetime64[ns, UTC]')

C:\D\Projects\Github\pandas\pandas\core\frame.py in __init__(self, data, index,
columns, dtype, copy)
252             else:
253                 mgr = self._init_ndarray(data, index, columns, dtype=dty
pe,
--> 254                                          copy=copy)
255         elif isinstance(data, (list, types.GeneratorType)):
256             if isinstance(data, types.GeneratorType):

C:\D\Projects\Github\pandas\pandas\core\frame.py in _init_ndarray(self, values,
index, columns, dtype, copy)
412
413         if dtype is not None:
--> 414             if values.dtype != dtype:
415                 try:
416                     values = values.astype(dtype)

TypeError: data type not understood

# Fixed code
def init_ndarray(values, index, columns, dtype=None, copy=False):
    # input must be a ndarray, list, Series, index

    if isinstance(values, ABCSeries):
        if columns is None:
            if values.name is not None:
                columns = [values.name]
        if index is None:
            index = values.index
        else:
            values = values.reindex(index)

        # zero len case (GH #2234)
        if not len(values) and columns is not None and len(columns):
            values = np.empty((0, 1), dtype=object)

    # we could have a categorical type passed or coerced to 'category'
    # recast this to an arrays_to_mgr
    if is_categorical_dtype(getattr(values, "dtype", None)) or is_categorical_dtype(
        dtype
    ):
        if not hasattr(values, "dtype"):
            values = prep_ndarray(values, copy=copy)
            values = values.ravel()
        elif copy:
            values = values.copy()

        index, columns = _get_axes(len(values), 1, index, columns)
        return arrays_to_mgr([values], columns, index, columns, dtype=dtype)
    elif is_extension_array_dtype(values) or is_extension_array_dtype(dtype):
        # GH#19157

        if isinstance(values, np.ndarray) and values.ndim > 1:
            # GH#12513 a EA dtype passed with a 2D array, split into
            #  multiple EAs that view the values
            values = [values[:, n] for n in range(values.shape[1])]
        else:
            values = [values]

        if columns is None:
            columns = list(range(len(values)))
        return arrays_to_mgr(values, columns, index, columns, dtype=dtype)

    # by definition an array here
    # the dtypes will be coerced to a single dtype
    values = prep_ndarray(values, copy=copy)

    if dtype is not None:
        if not is_dtype_equal(values.dtype, dtype):
            try:
                values = values.astype(dtype)
            except Exception as orig:
                # e.g. ValueError when trying to cast object dtype to float64
                raise ValueError(
                    f"failed to cast to '{dtype}' (Exception was: {orig})"
                ) from orig

    index, columns = _get_axes(*values.shape, index=index, columns=columns)
    values = values.T

    # if we don't have a dtype specified, then try to convert objects
    # on the entire block; this is to convert if we have datetimelike's
    # embedded in an object type
    if dtype is None and is_object_dtype(values):
        if values.ndim == 2 and values.shape[0] != 1:
            # transpose and separate blocks

            dvals_list = [maybe_infer_to_datetimelike(row) for row in values]
            for n in range(len(dvals_list)):
                if isinstance(dvals_list[n], np.ndarray):
                    dvals_list[n] = dvals_list[n].reshape(1, -1)

            from pandas.core.internals.blocks import make_block

            # TODO: What about re-joining object columns?
            block_values = [
                make_block(dvals_list[n], placement=[n]) for n in range(len(dvals_list))
            ]

        else:
            datelike_vals = maybe_infer_to_datetimelike(values)
            block_values = [datelike_vals]
    else:
        block_values = [values]

    return create_block_manager_from_blocks(block_values, [columns, index])